Feature selection in machine learning: A new perspective
High-dimensional data analysis is a challenge for researchers and engineers in the fields of machine learning and data mining. Feature selection provides an effective way to solve this problem by removing irrelevant and redundant data, which can reduce computation time, improve learning accuracy, an...
Saved in:
| Published in: | Neurocomputing (Amsterdam) Vol. 300; pp. 70 - 79 |
|---|---|
| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier B.V
26.07.2018
|
| Subjects: | |
| ISSN: | 0925-2312, 1872-8286 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | High-dimensional data analysis is a challenge for researchers and engineers in the fields of machine learning and data mining. Feature selection provides an effective way to solve this problem by removing irrelevant and redundant data, which can reduce computation time, improve learning accuracy, and facilitate a better understanding for the learning model or data. In this study, we discuss several frequently-used evaluation measures for feature selection, and then survey supervised, unsupervised, and semi-supervised feature selection methods, which are widely applied in machine learning problems, such as classification and clustering. Lastly, future challenges about feature selection are discussed. |
|---|---|
| AbstractList | High-dimensional data analysis is a challenge for researchers and engineers in the fields of machine learning and data mining. Feature selection provides an effective way to solve this problem by removing irrelevant and redundant data, which can reduce computation time, improve learning accuracy, and facilitate a better understanding for the learning model or data. In this study, we discuss several frequently-used evaluation measures for feature selection, and then survey supervised, unsupervised, and semi-supervised feature selection methods, which are widely applied in machine learning problems, such as classification and clustering. Lastly, future challenges about feature selection are discussed. |
| Author | Yang, Sheng Luo, Jiawei Wang, Shulin Cai, Jie |
| Author_xml | – sequence: 1 givenname: Jie surname: Cai fullname: Cai, Jie – sequence: 2 givenname: Jiawei surname: Luo fullname: Luo, Jiawei – sequence: 3 givenname: Shulin surname: Wang fullname: Wang, Shulin – sequence: 4 givenname: Sheng surname: Yang fullname: Yang, Sheng email: yangsh0506@sina.com |
| BookMark | eNqFz81KAzEUhuEgFWyrd-AiNzBj_ibJdCGUYlUouNF1SJMzmjLNlGRa8e6doa5c6OpszvvBM0OT2EVA6JaSkhIq73ZlhKPr9iUjVJWUlkSpCzSlWrFCMy0naEpqVhWMU3aFZjnvyPBIWT1Feg22PybAGVpwfegiDhHvrfsIEXALNsUQ3xd4iSN84gOkfBjfTnCNLhvbZrj5uXP0tn54XT0Vm5fH59VyUziuWF8IRq10XrmKbDWXtWu2TSWkEpYIWXkuoNGVo8QSJUE4720NXFdMgnVaec7nSJx3XepyTtCYQwp7m74MJWbUm505682oN5SaQT9ki1-ZC70dfX2yof0vvj_HMMBOAZLJLkB04EMa9MZ34e-Bb9SCeqg |
| CitedBy_id | crossref_primary_10_1016_j_eja_2025_127629 crossref_primary_10_1186_s12893_024_02427_x crossref_primary_10_1016_j_mtcomm_2025_112842 crossref_primary_10_1016_j_future_2019_12_034 crossref_primary_10_1007_s41060_023_00439_z crossref_primary_10_1016_j_asoc_2022_109969 crossref_primary_10_1007_s40747_023_01039_x crossref_primary_10_2196_10019 crossref_primary_10_3390_app131911067 crossref_primary_10_1007_s40200_025_01661_1 crossref_primary_10_1016_j_radi_2024_06_016 crossref_primary_10_1039_D4EM00742E crossref_primary_10_3390_diagnostics13223439 crossref_primary_10_1016_j_eswa_2023_120624 crossref_primary_10_1016_j_compag_2024_109495 crossref_primary_10_1007_s00521_022_07916_9 crossref_primary_10_1016_j_cjche_2018_11_008 crossref_primary_10_1016_j_engappai_2025_112000 crossref_primary_10_1134_S1054661822040058 crossref_primary_10_1061_JITSE4_ISENG_2477 crossref_primary_10_1016_j_jaap_2024_106512 crossref_primary_10_1016_j_knosys_2022_109655 crossref_primary_10_1093_jrr_rrae033 crossref_primary_10_1155_2022_8493004 crossref_primary_10_1002_agr_21773 crossref_primary_10_1016_j_jhydrol_2023_129200 crossref_primary_10_1016_j_epsr_2023_109848 crossref_primary_10_3390_s23115243 crossref_primary_10_1002_aenm_202501857 crossref_primary_10_1186_s12903_024_03910_w crossref_primary_10_1007_s10489_025_06558_3 crossref_primary_10_1007_s13042_021_01347_z crossref_primary_10_1016_j_measurement_2021_109021 crossref_primary_10_1007_s12530_023_09507_y crossref_primary_10_1016_j_eswa_2022_118255 crossref_primary_10_1093_nsr_nwz190 crossref_primary_10_1016_j_ijleo_2023_170957 crossref_primary_10_1093_bib_bbz091 crossref_primary_10_1007_s12652_025_05008_9 crossref_primary_10_1016_j_iot_2022_100541 crossref_primary_10_1017_dap_2023_46 crossref_primary_10_1007_s41066_019_00162_w crossref_primary_10_1016_j_neucom_2024_128150 crossref_primary_10_1080_10106049_2021_1967464 crossref_primary_10_3390_s18072166 crossref_primary_10_1016_j_ces_2024_119955 crossref_primary_10_3390_en16134922 crossref_primary_10_1016_j_neucom_2021_03_002 crossref_primary_10_1002_adts_202000029 crossref_primary_10_1007_s00404_022_06864_y crossref_primary_10_1109_JSTSP_2024_3378887 crossref_primary_10_14423_SMJ_0000000000001427 crossref_primary_10_1016_j_mtchem_2025_102640 crossref_primary_10_1016_j_chemosphere_2021_131954 crossref_primary_10_1016_j_envsoft_2024_106053 crossref_primary_10_3390_info15010057 crossref_primary_10_1038_s41598_024_82838_1 crossref_primary_10_1109_TFUZZ_2024_3437367 crossref_primary_10_1049_ntw2_12077 crossref_primary_10_3233_JIFS_189461 crossref_primary_10_1007_s00521_021_05991_y crossref_primary_10_1007_s42417_024_01470_5 crossref_primary_10_1016_j_knosys_2022_108787 crossref_primary_10_1007_s13042_022_01616_5 crossref_primary_10_1155_2022_5869529 crossref_primary_10_3390_s21072368 crossref_primary_10_1007_s40614_020_00270_y crossref_primary_10_1111_exsy_13327 crossref_primary_10_1016_j_engappai_2024_107865 crossref_primary_10_1186_s40537_024_00958_x crossref_primary_10_1007_s13369_022_06590_2 crossref_primary_10_1109_ACCESS_2023_3280122 crossref_primary_10_5194_nhess_23_1665_2023 crossref_primary_10_1016_j_cie_2020_106536 crossref_primary_10_1016_j_cca_2025_120165 crossref_primary_10_3390_bs13050427 crossref_primary_10_1007_s00766_024_00416_3 crossref_primary_10_1016_j_compbiomed_2023_107197 crossref_primary_10_1016_j_scs_2019_101438 crossref_primary_10_3390_bdcc5030036 crossref_primary_10_1371_journal_pone_0318874 crossref_primary_10_1016_j_eswa_2021_114658 crossref_primary_10_1109_ACCESS_2024_3376235 crossref_primary_10_3390_diagnostics11101787 crossref_primary_10_3390_en16114271 crossref_primary_10_1371_journal_pcbi_1010180 crossref_primary_10_3390_s22103819 crossref_primary_10_1016_j_envpol_2024_123763 crossref_primary_10_1088_1361_6501_ad6890 crossref_primary_10_1371_journal_pone_0272956 crossref_primary_10_3390_e21121179 crossref_primary_10_3390_electronics10151757 crossref_primary_10_3390_fi12100177 crossref_primary_10_3390_s21144941 crossref_primary_10_1093_bib_bbac537 crossref_primary_10_1155_2022_3879266 crossref_primary_10_1109_ACCESS_2021_3049815 crossref_primary_10_1007_s10586_023_04001_1 crossref_primary_10_1016_j_jprot_2025_105439 crossref_primary_10_1109_ACCESS_2025_3551772 crossref_primary_10_1109_ACCESS_2020_3046190 crossref_primary_10_1016_j_ribaf_2023_101913 crossref_primary_10_14358_PERS_21_00030R2 crossref_primary_10_1007_s11069_024_06878_6 crossref_primary_10_1007_s11227_025_07163_4 crossref_primary_10_3390_su13116199 crossref_primary_10_1007_s10489_022_03971_w crossref_primary_10_1049_cit2_12317 crossref_primary_10_1088_1361_6579_adce52 crossref_primary_10_1016_j_earscirev_2022_103944 crossref_primary_10_1016_j_bspc_2023_105309 crossref_primary_10_1002_crat_202200064 crossref_primary_10_1088_1741_2552_acb088 crossref_primary_10_1109_JSTARS_2023_3298996 crossref_primary_10_1016_j_matdes_2022_111483 crossref_primary_10_1007_s41870_024_01974_z crossref_primary_10_1016_j_jelectrocard_2020_04_011 crossref_primary_10_1111_pai_13247 crossref_primary_10_2478_abm_2024_0029 crossref_primary_10_3390_sym15010140 crossref_primary_10_1016_j_neucom_2020_07_028 crossref_primary_10_3390_s23187710 crossref_primary_10_1007_s44163_025_00305_w crossref_primary_10_1016_j_compag_2023_108064 crossref_primary_10_1016_j_engappai_2025_110041 crossref_primary_10_1016_j_iotcps_2025_05_002 crossref_primary_10_1007_s40436_024_00498_w crossref_primary_10_1007_s00521_025_11273_8 crossref_primary_10_1016_j_aquatox_2023_106429 crossref_primary_10_1007_s10489_021_03118_3 crossref_primary_10_1016_j_cie_2022_108022 crossref_primary_10_3390_app132413319 crossref_primary_10_3390_rs17172955 crossref_primary_10_1109_ACCESS_2023_3294563 crossref_primary_10_3390_su14074294 crossref_primary_10_1038_s41467_025_60897_w crossref_primary_10_1007_s10489_020_01765_6 crossref_primary_10_1016_j_comcom_2023_02_019 crossref_primary_10_1097_MNM_0000000000002003 crossref_primary_10_1038_s41528_023_00286_9 crossref_primary_10_1016_j_future_2024_05_013 crossref_primary_10_1109_JIOT_2021_3101231 crossref_primary_10_3390_w17081131 crossref_primary_10_1186_s12859_021_04049_z crossref_primary_10_3390_s22093566 crossref_primary_10_1016_j_engappai_2023_106644 crossref_primary_10_1186_s12911_023_02142_2 crossref_primary_10_1002_adfm_202425588 crossref_primary_10_1038_s41598_023_30941_0 crossref_primary_10_1177_15553434221122899 crossref_primary_10_1093_bib_bbab410 crossref_primary_10_1016_j_patcog_2020_107760 crossref_primary_10_3389_fgene_2023_1054032 crossref_primary_10_1016_j_bspc_2023_105920 crossref_primary_10_1109_TKDE_2024_3397878 crossref_primary_10_1016_j_compchemeng_2019_106656 crossref_primary_10_7717_peerj_cs_2528 crossref_primary_10_3390_rs14246376 crossref_primary_10_1016_j_knosys_2020_106429 crossref_primary_10_1111_jocs_15917 crossref_primary_10_1016_j_jksuci_2023_101704 crossref_primary_10_3390_biomimetics8020266 crossref_primary_10_3389_fneur_2019_00996 crossref_primary_10_3390_e23101274 crossref_primary_10_1109_ACCESS_2024_3435333 crossref_primary_10_3390_biom15010081 crossref_primary_10_1093_cercor_bhac213 crossref_primary_10_1111_epi_18605 crossref_primary_10_1371_journal_pone_0295801 crossref_primary_10_3390_genes13101916 crossref_primary_10_1016_j_solener_2020_09_071 crossref_primary_10_1016_j_aap_2023_107022 crossref_primary_10_1186_s12911_025_02995_9 crossref_primary_10_1016_j_compchemeng_2024_108623 crossref_primary_10_32604_cmc_2022_024566 crossref_primary_10_1016_j_iot_2024_101110 crossref_primary_10_1109_ACCESS_2024_3450968 crossref_primary_10_3390_rs12122028 crossref_primary_10_1016_j_jmsy_2021_03_014 crossref_primary_10_1038_s41398_021_01669_0 crossref_primary_10_1016_j_ymben_2022_12_009 crossref_primary_10_3390_app142310965 crossref_primary_10_1007_s42979_024_03308_z crossref_primary_10_1109_ACCESS_2020_3033784 crossref_primary_10_3389_fendo_2022_1011492 crossref_primary_10_1016_j_cmpb_2021_105942 crossref_primary_10_1007_s00521_022_07755_8 crossref_primary_10_1016_j_nxmate_2024_100145 crossref_primary_10_1002_aaai_12135 crossref_primary_10_1007_s11783_023_1748_3 crossref_primary_10_1016_j_geoderma_2020_114552 crossref_primary_10_1007_s10660_022_09624_x crossref_primary_10_1016_j_ijar_2021_01_003 crossref_primary_10_1016_j_artmed_2022_102406 crossref_primary_10_1061_IJGNAI_GMENG_9026 crossref_primary_10_1007_s10489_021_02405_3 crossref_primary_10_1016_j_ijfatigue_2025_109117 crossref_primary_10_1016_j_actamat_2021_117431 crossref_primary_10_1016_j_asoc_2022_109355 crossref_primary_10_1016_j_indcrop_2025_121289 crossref_primary_10_1049_ipr2_12365 crossref_primary_10_1016_j_rsase_2020_100425 crossref_primary_10_1016_j_egyr_2022_10_125 crossref_primary_10_1007_s42947_022_00213_7 crossref_primary_10_3390_app12178654 crossref_primary_10_1109_ACCESS_2021_3117891 crossref_primary_10_1016_j_apenergy_2024_125221 crossref_primary_10_1186_s12938_024_01219_x crossref_primary_10_1016_j_cemconcomp_2024_105488 crossref_primary_10_1093_comjnl_bxae068 crossref_primary_10_3390_app11073273 crossref_primary_10_3390_agronomy13123070 crossref_primary_10_1016_j_jksuci_2023_101737 crossref_primary_10_1177_1088467X251370595 crossref_primary_10_1093_comjnl_bxz161 crossref_primary_10_1093_comjnl_bxz163 crossref_primary_10_1016_j_cma_2022_115845 crossref_primary_10_1016_j_future_2025_107827 crossref_primary_10_1109_ACCESS_2020_2970023 crossref_primary_10_1109_TIV_2023_3314788 crossref_primary_10_3390_s22134716 crossref_primary_10_3390_s25144515 crossref_primary_10_1016_j_chemosphere_2024_141584 crossref_primary_10_1016_j_asoc_2020_106537 crossref_primary_10_32604_cmc_2023_032287 crossref_primary_10_3390_su141912485 crossref_primary_10_1016_j_knosys_2023_110577 crossref_primary_10_1016_j_asoc_2021_107907 crossref_primary_10_1155_2022_5988567 crossref_primary_10_1109_TIM_2021_3121473 crossref_primary_10_1016_j_aap_2024_107740 crossref_primary_10_1016_j_compind_2025_104273 crossref_primary_10_1016_j_copsyc_2020_05_002 crossref_primary_10_1109_ACCESS_2021_3126777 crossref_primary_10_1007_s44196_025_00881_w crossref_primary_10_1007_s11042_022_12913_0 crossref_primary_10_1007_s40200_023_01357_4 crossref_primary_10_1016_j_engappai_2020_104099 crossref_primary_10_3390_agronomy15020492 crossref_primary_10_1186_s12938_022_01049_9 crossref_primary_10_1016_j_ecolind_2021_107499 crossref_primary_10_3390_en18153955 crossref_primary_10_1007_s40735_025_01000_0 crossref_primary_10_1016_j_cirp_2024_05_004 crossref_primary_10_1109_ACCESS_2022_3165814 crossref_primary_10_1016_j_cageo_2024_105788 crossref_primary_10_1080_09585192_2025_2480617 crossref_primary_10_3390_app14083254 crossref_primary_10_3390_biomedinformatics5030054 crossref_primary_10_1155_acis_2766701 crossref_primary_10_1155_2021_3569632 crossref_primary_10_1002_ejsc_70042 crossref_primary_10_1016_j_psychres_2025_116353 crossref_primary_10_32604_cmc_2020_011969 crossref_primary_10_1111_exsy_13111 crossref_primary_10_7717_peerj_cs_850 crossref_primary_10_1016_j_neucom_2021_10_079 crossref_primary_10_1080_1062936X_2023_2278074 crossref_primary_10_1016_j_neucom_2023_126585 crossref_primary_10_1186_s12889_024_17978_0 crossref_primary_10_1016_j_future_2023_01_027 crossref_primary_10_7717_peerj_cs_2306 crossref_primary_10_1016_j_eneco_2020_104790 crossref_primary_10_1016_j_envpol_2021_118153 crossref_primary_10_1080_02664763_2020_1815672 crossref_primary_10_1016_j_egyai_2025_100483 crossref_primary_10_1016_j_ejrad_2024_111297 crossref_primary_10_3390_agriculture13030596 crossref_primary_10_1007_s00170_024_14543_2 crossref_primary_10_1016_j_inffus_2023_101959 crossref_primary_10_1111_1752_1688_13186 crossref_primary_10_1016_j_patcog_2022_109096 crossref_primary_10_1109_TGRS_2023_3333689 crossref_primary_10_1016_j_compbiomed_2025_110472 crossref_primary_10_1016_j_knosys_2022_110249 crossref_primary_10_1080_09599916_2019_1587489 crossref_primary_10_1007_s11227_025_07021_3 crossref_primary_10_3847_PSJ_ad25fd crossref_primary_10_1007_s10586_024_04996_1 crossref_primary_10_3390_ijerph182111347 crossref_primary_10_61186_jsdp_22_1_39 crossref_primary_10_1016_j_rvsc_2024_105201 crossref_primary_10_1007_s10462_022_10274_6 crossref_primary_10_1039_D2CY00284A crossref_primary_10_1088_1612_202X_ab7a9a crossref_primary_10_1016_j_xfre_2022_04_004 crossref_primary_10_1109_ACCESS_2019_2909945 crossref_primary_10_1371_journal_pone_0289318 crossref_primary_10_3233_IDA_205331 crossref_primary_10_1109_ACCESS_2021_3119404 crossref_primary_10_1002_ange_202502410 crossref_primary_10_1016_j_brs_2021_09_009 crossref_primary_10_1007_s40614_023_00376_z crossref_primary_10_1088_2040_8986_abfee0 crossref_primary_10_1111_mice_12992 crossref_primary_10_1016_j_jallcom_2023_172994 crossref_primary_10_1007_s41315_022_00244_0 crossref_primary_10_3390_app13021143 crossref_primary_10_1155_2022_1562942 crossref_primary_10_1016_j_asoc_2023_110319 crossref_primary_10_3390_math12030372 crossref_primary_10_1007_s00521_022_07869_z crossref_primary_10_1016_j_jece_2025_118842 crossref_primary_10_1016_j_ultras_2022_106854 crossref_primary_10_1051_itmconf_20257003020 crossref_primary_10_1016_j_rineng_2025_105629 crossref_primary_10_15446_dyna_v87n212_80202 crossref_primary_10_3390_biomedicines10092220 crossref_primary_10_1109_TETCI_2024_3451695 crossref_primary_10_1016_j_radonc_2025_110872 crossref_primary_10_1016_j_isprsjprs_2024_08_015 crossref_primary_10_1109_TKDE_2023_3332671 crossref_primary_10_1093_nargab_lqae184 crossref_primary_10_1007_s11831_020_09524_z crossref_primary_10_3390_rs13030531 crossref_primary_10_3390_s24175752 crossref_primary_10_1016_j_asoc_2022_109046 crossref_primary_10_3390_app11094117 crossref_primary_10_1007_s40747_021_00521_8 crossref_primary_10_1016_j_addma_2022_102621 crossref_primary_10_1016_j_ins_2023_120059 crossref_primary_10_1016_j_measurement_2023_112465 crossref_primary_10_1109_ACCESS_2022_3191115 crossref_primary_10_3390_sym15101834 crossref_primary_10_1016_j_neuroimage_2022_119787 crossref_primary_10_1016_j_bspc_2024_106795 crossref_primary_10_1080_09377255_2025_2538005 crossref_primary_10_3390_s22062206 crossref_primary_10_1007_s42484_024_00213_9 crossref_primary_10_1016_j_patcog_2022_108912 crossref_primary_10_1109_ACCESS_2018_2878855 crossref_primary_10_1016_j_techfore_2020_120175 crossref_primary_10_1051_jnwpu_20203830471 crossref_primary_10_3390_drones8020061 crossref_primary_10_1155_2021_9294356 crossref_primary_10_2166_wst_2024_142 crossref_primary_10_1371_journal_pcbi_1011649 crossref_primary_10_1016_j_imu_2019_100255 crossref_primary_10_1109_ACCESS_2023_3287326 crossref_primary_10_1021_acs_chemrestox_4c00523 crossref_primary_10_1371_journal_pone_0220842 crossref_primary_10_1007_s11356_025_36582_2 crossref_primary_10_1186_s12911_024_02590_4 crossref_primary_10_1007_s41870_023_01271_1 crossref_primary_10_1016_j_asoc_2020_106426 crossref_primary_10_1016_j_fss_2020_03_003 crossref_primary_10_1016_j_scs_2020_102320 crossref_primary_10_1016_j_jweia_2021_104788 crossref_primary_10_1155_2022_2893486 crossref_primary_10_3390_info16010034 crossref_primary_10_2478_amns_2025_0805 crossref_primary_10_1007_s10489_020_01800_6 crossref_primary_10_1007_s12553_023_00732_8 crossref_primary_10_1016_j_jsv_2023_117769 crossref_primary_10_1086_731788 crossref_primary_10_3390_s24227193 crossref_primary_10_1016_j_cscm_2023_e02554 crossref_primary_10_1038_s41598_025_10296_4 crossref_primary_10_3390_e22080876 crossref_primary_10_1080_10095020_2025_2542969 crossref_primary_10_1007_s11042_022_12161_2 crossref_primary_10_1109_ACCESS_2020_2990375 crossref_primary_10_3390_s22030907 crossref_primary_10_1007_s00500_022_07234_1 crossref_primary_10_1016_j_measurement_2023_113335 crossref_primary_10_1016_j_compag_2022_107275 crossref_primary_10_1016_j_compind_2020_103345 crossref_primary_10_1002_adma_202208683 crossref_primary_10_1093_jbmr_zjaf105 crossref_primary_10_1016_j_aei_2024_102799 crossref_primary_10_3390_ijerph17030731 crossref_primary_10_1111_ppa_13902 crossref_primary_10_3390_info15100593 crossref_primary_10_1038_s41598_025_93417_3 crossref_primary_10_1108_IJPCC_04_2020_0018 crossref_primary_10_1186_s12876_024_03223_w crossref_primary_10_1002_hsr2_70327 crossref_primary_10_1007_s00521_024_10166_6 crossref_primary_10_1038_s41598_023_48418_5 crossref_primary_10_1016_j_rse_2020_112105 crossref_primary_10_1080_09613218_2023_2204418 crossref_primary_10_3390_rs17060988 crossref_primary_10_1016_j_tust_2024_105945 crossref_primary_10_1016_j_oceaneng_2023_115277 crossref_primary_10_3390_machines12060368 crossref_primary_10_1016_j_eswa_2022_117520 crossref_primary_10_1016_j_engfailanal_2022_106248 crossref_primary_10_1016_j_uclim_2024_101903 crossref_primary_10_3390_ma16145036 crossref_primary_10_1007_s00521_022_07669_5 crossref_primary_10_1093_jcde_qwac028 crossref_primary_10_3390_cancers14163914 crossref_primary_10_1371_journal_pdig_0000780 crossref_primary_10_1016_j_trc_2019_03_004 crossref_primary_10_3390_healthcare11030319 crossref_primary_10_1155_2022_1693365 crossref_primary_10_1016_j_chemolab_2021_104396 crossref_primary_10_1109_ACCESS_2023_3307138 crossref_primary_10_1016_j_ress_2024_110387 crossref_primary_10_1038_s41598_025_06426_7 crossref_primary_10_1016_j_compbiomed_2020_103667 crossref_primary_10_1109_ACCESS_2021_3073261 crossref_primary_10_1016_j_cmpb_2021_106038 crossref_primary_10_1007_s13042_022_01589_5 crossref_primary_10_1016_j_ecoinf_2024_102832 crossref_primary_10_32604_cmc_2022_026619 crossref_primary_10_1007_s13755_021_00164_6 crossref_primary_10_1016_j_aiig_2025_100144 crossref_primary_10_1007_s11665_025_11115_7 crossref_primary_10_1016_j_asoc_2019_105859 crossref_primary_10_1002_eqe_4068 crossref_primary_10_1016_j_matpr_2021_04_643 crossref_primary_10_1016_j_mtener_2024_101769 crossref_primary_10_1007_s11517_020_02301_x crossref_primary_10_3390_make3020020 crossref_primary_10_3389_fbuil_2024_1343398 crossref_primary_10_1016_j_engappai_2022_105728 crossref_primary_10_1016_j_eswa_2019_113024 crossref_primary_10_1007_s11356_024_34440_1 crossref_primary_10_1016_j_knosys_2020_106365 crossref_primary_10_2196_19679 crossref_primary_10_1109_ACCESS_2021_3097206 crossref_primary_10_3390_app8101927 crossref_primary_10_1007_s10462_022_10195_4 crossref_primary_10_3390_ijms26062428 crossref_primary_10_3390_math12010113 crossref_primary_10_3390_sym12010067 crossref_primary_10_1007_s10489_021_03008_8 crossref_primary_10_1016_j_ibmed_2025_100255 crossref_primary_10_1016_j_jastp_2025_106448 crossref_primary_10_1038_s41598_025_03688_z crossref_primary_10_1109_ACCESS_2024_3352070 crossref_primary_10_1007_s00500_025_10530_1 crossref_primary_10_3390_batteries11090322 crossref_primary_10_1016_j_suscom_2022_100838 crossref_primary_10_1016_j_cose_2021_102414 crossref_primary_10_3389_fphar_2020_00639 crossref_primary_10_3390_s18124425 crossref_primary_10_1016_j_suronc_2024_102079 crossref_primary_10_1016_j_neucom_2024_128427 crossref_primary_10_3390_app142210532 crossref_primary_10_1016_j_sab_2019_105721 crossref_primary_10_1109_TII_2024_3507944 crossref_primary_10_1007_s10479_021_04415_3 crossref_primary_10_1007_s10044_020_00916_2 crossref_primary_10_3390_ijgi9040227 crossref_primary_10_1016_j_chroma_2023_464467 crossref_primary_10_3389_fmicb_2020_623788 crossref_primary_10_1021_acs_analchem_4c06762 crossref_primary_10_1016_j_ress_2021_108290 crossref_primary_10_1109_ACCESS_2024_3523864 crossref_primary_10_1016_j_chemolab_2023_104989 crossref_primary_10_3233_JIFS_213112 crossref_primary_10_1080_0952813X_2023_2183273 crossref_primary_10_1007_s12145_023_00951_7 crossref_primary_10_1016_j_jenvman_2024_120559 crossref_primary_10_1080_0951192X_2022_2027019 crossref_primary_10_23876_j_krcp_25_046 crossref_primary_10_3390_ijerph21111474 crossref_primary_10_1016_j_cirp_2019_03_008 crossref_primary_10_3390_s21175753 crossref_primary_10_1016_j_envpol_2021_117711 crossref_primary_10_1016_j_media_2022_102368 crossref_primary_10_1007_s00521_024_10171_9 crossref_primary_10_1109_ACCESS_2022_3193935 crossref_primary_10_3233_IDA_205154 crossref_primary_10_3390_atmos13071055 crossref_primary_10_1088_1755_1315_655_1_012025 crossref_primary_10_1049_cit2_12327 crossref_primary_10_1016_j_engappai_2020_103557 crossref_primary_10_1016_j_trac_2022_116859 crossref_primary_10_1007_s10311_024_01700_y crossref_primary_10_1016_j_trac_2022_116863 crossref_primary_10_1080_00207543_2024_2384597 crossref_primary_10_1016_j_jer_2024_02_018 crossref_primary_10_1016_j_energy_2022_123255 crossref_primary_10_3389_fimmu_2023_1137850 crossref_primary_10_1371_journal_pcbi_1010718 crossref_primary_10_3390_app15158196 crossref_primary_10_1186_s12859_022_04962_x crossref_primary_10_1016_j_urbmob_2025_100104 crossref_primary_10_1177_0047287520934871 crossref_primary_10_1007_s10489_019_01513_5 crossref_primary_10_1016_j_resconrec_2020_105381 crossref_primary_10_1007_s13755_023_00251_w crossref_primary_10_1080_09544828_2018_1463514 crossref_primary_10_1016_j_engappai_2023_107168 crossref_primary_10_3389_fcimb_2022_882995 crossref_primary_10_3390_healthcare10071256 crossref_primary_10_1007_s11707_020_0848_7 crossref_primary_10_1017_S0033291724000862 crossref_primary_10_1088_1742_6596_2703_1_012031 crossref_primary_10_1080_24705314_2023_2230398 crossref_primary_10_1016_j_cmpb_2024_108477 crossref_primary_10_1016_j_knosys_2025_113940 crossref_primary_10_1145_3588961 crossref_primary_10_1088_1757_899X_919_4_042018 crossref_primary_10_1016_j_conbuildmat_2023_130670 crossref_primary_10_1007_s44163_025_00224_w crossref_primary_10_1002_clc_23963 crossref_primary_10_1007_s00500_019_03911_w crossref_primary_10_1088_1742_6596_1299_1_012035 crossref_primary_10_3390_rs17162843 crossref_primary_10_1016_j_knosys_2023_111163 crossref_primary_10_1016_j_jag_2023_103446 crossref_primary_10_1016_j_knosys_2021_107326 crossref_primary_10_1109_ACCESS_2020_2966017 crossref_primary_10_1016_j_conbuildmat_2021_124894 crossref_primary_10_1016_j_cortex_2024_11_022 crossref_primary_10_1016_j_jhydrol_2025_133237 crossref_primary_10_1007_s00521_020_05321_8 crossref_primary_10_1145_3639063 crossref_primary_10_1016_j_ecoinf_2021_101231 crossref_primary_10_1088_1361_648X_abb895 crossref_primary_10_1007_s10489_021_02257_x crossref_primary_10_1016_j_heliyon_2023_e19525 crossref_primary_10_1061_JWRMD5_WRENG_5620 crossref_primary_10_1155_2022_5728880 crossref_primary_10_3390_agronomy13030792 crossref_primary_10_1111_tgis_70028 crossref_primary_10_1016_j_eswa_2020_113859 crossref_primary_10_1002_anie_202502410 crossref_primary_10_1371_journal_pone_0218760 crossref_primary_10_1002_idm2_12180 crossref_primary_10_1038_s41598_024_55902_z crossref_primary_10_1155_2021_5069016 crossref_primary_10_1109_ACCESS_2022_3153038 crossref_primary_10_1016_j_heliyon_2024_e27411 crossref_primary_10_1109_TCBB_2022_3149864 crossref_primary_10_3390_ijerph19031858 crossref_primary_10_1007_s42452_024_06444_6 crossref_primary_10_1140_epjds_s13688_024_00512_y crossref_primary_10_1016_j_ecoinf_2025_103166 crossref_primary_10_1155_2022_4986826 crossref_primary_10_3390_su132212746 crossref_primary_10_3389_fmed_2021_635771 crossref_primary_10_1016_j_buildenv_2023_110722 crossref_primary_10_1109_ACCESS_2019_2957779 crossref_primary_10_1016_j_asoc_2024_112234 crossref_primary_10_1016_j_eswa_2022_117116 crossref_primary_10_1016_j_ijleo_2022_170340 crossref_primary_10_15857_ksep_2021_30_2_131 crossref_primary_10_1007_s10270_021_00920_y crossref_primary_10_1007_s10462_021_10072_6 crossref_primary_10_1007_s10919_020_00332_4 crossref_primary_10_1016_j_scitotenv_2024_171733 crossref_primary_10_2478_ijcss_2022_0007 crossref_primary_10_3390_math10122039 crossref_primary_10_1016_j_specom_2023_102960 crossref_primary_10_1016_j_knosys_2025_113978 crossref_primary_10_1007_s11356_023_26611_3 crossref_primary_10_1089_met_2019_0052 crossref_primary_10_1007_s43674_021_00008_6 crossref_primary_10_1038_s41545_025_00504_z crossref_primary_10_1016_j_rineng_2025_105693 crossref_primary_10_1016_j_trac_2021_116372 crossref_primary_10_32604_cmc_2023_038045 crossref_primary_10_1007_s13369_021_06456_z crossref_primary_10_1016_j_apenergy_2023_122413 crossref_primary_10_3389_fnhum_2021_700627 crossref_primary_10_5772_acrt_01 crossref_primary_10_1016_j_rser_2025_115817 crossref_primary_10_1080_13632469_2024_2400194 crossref_primary_10_2174_0126662558324883241011101625 crossref_primary_10_1109_JBHI_2019_2953978 crossref_primary_10_3390_ma17081793 crossref_primary_10_3390_app13074119 crossref_primary_10_3390_atmos12020238 crossref_primary_10_1016_j_jenvman_2021_113344 crossref_primary_10_3390_math10040553 crossref_primary_10_1080_08839514_2022_2138124 crossref_primary_10_1016_j_agwat_2024_109059 crossref_primary_10_1016_j_talanta_2023_125021 crossref_primary_10_1155_2022_9261713 crossref_primary_10_1016_j_asoc_2021_108006 crossref_primary_10_1007_s11831_025_10284_x crossref_primary_10_3390_e21060602 crossref_primary_10_1007_s10462_023_10546_9 crossref_primary_10_1016_j_jag_2025_104725 crossref_primary_10_1007_s10462_020_09860_3 crossref_primary_10_1016_j_fecs_2025_100368 crossref_primary_10_1016_j_measen_2024_101291 crossref_primary_10_1177_14759217221111141 crossref_primary_10_1177_14759217231166116 crossref_primary_10_1016_j_ecolind_2024_111693 crossref_primary_10_1016_j_jneumeth_2020_108853 crossref_primary_10_3390_rs14092086 crossref_primary_10_1016_j_compag_2023_107929 crossref_primary_10_3390_en13205369 crossref_primary_10_1016_j_measurement_2021_109145 crossref_primary_10_1007_s00234_019_02266_1 crossref_primary_10_1016_j_asoc_2024_112574 crossref_primary_10_1080_03081079_2023_2206654 crossref_primary_10_3233_IDA_215826 crossref_primary_10_1016_j_inffus_2025_103152 crossref_primary_10_3390_app14031023 crossref_primary_10_1007_s12264_024_01312_0 crossref_primary_10_1016_j_imu_2021_100564 crossref_primary_10_1016_j_imu_2022_100863 crossref_primary_10_1016_j_bspc_2023_105869 crossref_primary_10_1177_14604582241285769 crossref_primary_10_1016_j_ins_2023_119502 crossref_primary_10_1016_j_knosys_2022_109529 crossref_primary_10_1109_TNSE_2025_3553442 crossref_primary_10_1007_s10549_024_07375_x crossref_primary_10_1016_j_health_2022_100060 crossref_primary_10_1016_j_jbi_2024_104699 crossref_primary_10_1016_j_medengphy_2021_05_017 crossref_primary_10_1109_JIOT_2020_3016297 crossref_primary_10_1016_j_compbiomed_2024_109539 crossref_primary_10_1093_jrr_rrad052 crossref_primary_10_1155_2022_3878072 crossref_primary_10_1016_j_pmatsci_2023_101085 crossref_primary_10_1109_ACCESS_2020_3028469 crossref_primary_10_2166_hydro_2023_170 crossref_primary_10_1007_s10115_024_02258_5 crossref_primary_10_1016_j_bbi_2024_12_007 crossref_primary_10_1109_JSEN_2022_3155706 crossref_primary_10_3390_app12188974 crossref_primary_10_3390_fire7090296 crossref_primary_10_1007_s10853_022_07132_9 crossref_primary_10_1016_j_patcog_2025_111477 crossref_primary_10_1155_2023_5585130 crossref_primary_10_1016_j_compind_2022_103634 crossref_primary_10_1186_s44167_024_00045_9 crossref_primary_10_1007_s11042_025_20707_3 crossref_primary_10_1016_j_engappai_2024_108834 crossref_primary_10_1016_j_est_2024_113556 crossref_primary_10_2478_amns_2025_0135 crossref_primary_10_1016_j_bspc_2023_105850 crossref_primary_10_3389_fmicb_2021_763498 crossref_primary_10_3390_biomimetics8050417 crossref_primary_10_1155_2020_8571712 crossref_primary_10_1109_ACCESS_2022_3211505 crossref_primary_10_1016_j_jhydrol_2025_134255 crossref_primary_10_1016_j_compag_2022_106997 crossref_primary_10_1002_int_23044 crossref_primary_10_1016_j_heliyon_2023_e17982 crossref_primary_10_3390_biomimetics9080501 crossref_primary_10_1016_j_prrv_2021_06_002 crossref_primary_10_1007_s00603_025_04807_y crossref_primary_10_1007_s11219_023_09636_2 crossref_primary_10_1111_exsy_12527 crossref_primary_10_1007_s00382_021_06104_0 crossref_primary_10_2186_jpr_JPR_D_24_00250 crossref_primary_10_3390_math10040589 crossref_primary_10_1016_j_cities_2024_105115 crossref_primary_10_33889_IJMEMS_2025_10_5_077 crossref_primary_10_1016_j_heliyon_2024_e24797 crossref_primary_10_1109_ACCESS_2022_3177752 crossref_primary_10_1109_TNSRE_2023_3326065 crossref_primary_10_7717_peerj_cs_1997 crossref_primary_10_1021_acs_iecr_5c01985 crossref_primary_10_1021_acs_jpclett_5c01616 crossref_primary_10_1109_ACCESS_2019_2929487 crossref_primary_10_1007_s11666_021_01198_8 crossref_primary_10_1186_s43067_023_00101_5 crossref_primary_10_1186_s13040_021_00240_3 crossref_primary_10_1109_TIM_2023_3303233 crossref_primary_10_1088_2632_2153_ad513a crossref_primary_10_3389_fgene_2020_595912 crossref_primary_10_3390_s22103700 crossref_primary_10_1016_j_envres_2024_118146 crossref_primary_10_1016_j_autcon_2023_105058 crossref_primary_10_1016_j_engappai_2023_107425 crossref_primary_10_1109_ACCESS_2020_3004699 crossref_primary_10_3847_1538_4365_acf218 crossref_primary_10_1080_02648725_2023_2202524 crossref_primary_10_1007_s00521_025_11163_z crossref_primary_10_1007_s00500_020_05045_w crossref_primary_10_1007_s12065_021_00608_8 crossref_primary_10_1007_s00500_025_10913_4 crossref_primary_10_1155_2022_5415702 crossref_primary_10_1177_14759217251313815 crossref_primary_10_3390_app131810221 crossref_primary_10_3390_electronics12061508 crossref_primary_10_1016_j_biortech_2025_132968 crossref_primary_10_1109_ACCESS_2022_3190502 crossref_primary_10_1016_j_commatsci_2024_113024 crossref_primary_10_1016_j_jhydrol_2024_130628 crossref_primary_10_2118_208579_PA crossref_primary_10_1109_ACCESS_2021_3126834 crossref_primary_10_1093_bib_bbab354 crossref_primary_10_1016_j_bspc_2023_105423 crossref_primary_10_1039_D5CS00053J crossref_primary_10_1016_j_eswa_2022_119015 crossref_primary_10_1002_for_3284 crossref_primary_10_1007_s13042_024_02111_9 crossref_primary_10_1016_j_sysarc_2025_103536 crossref_primary_10_1155_2022_5034976 crossref_primary_10_1155_2021_3457967 crossref_primary_10_3390_rs13234832 crossref_primary_10_1016_j_asoc_2024_111426 crossref_primary_10_1093_bjr_tqaf051 crossref_primary_10_1155_2023_9713905 crossref_primary_10_3390_rs13030389 crossref_primary_10_1007_s10207_023_00790_z crossref_primary_10_1016_j_enbuild_2025_115991 crossref_primary_10_1038_s41598_024_69954_8 crossref_primary_10_3390_app14219870 crossref_primary_10_1016_j_eswa_2019_01_016 crossref_primary_10_1016_j_engappai_2021_104455 crossref_primary_10_1016_j_cjche_2020_10_044 crossref_primary_10_1088_2632_959X_ad4c80 crossref_primary_10_1155_2020_2860479 crossref_primary_10_62184_mmc_jmmc110020251 crossref_primary_10_1007_s11334_024_00581_2 crossref_primary_10_1016_j_ccr_2024_216329 crossref_primary_10_1016_j_compbiomed_2022_105639 crossref_primary_10_1111_exsy_13400 crossref_primary_10_1016_j_eswa_2022_117064 crossref_primary_10_1016_j_jag_2024_104202 crossref_primary_10_3390_ai3010001 crossref_primary_10_3390_app12199413 crossref_primary_10_1186_s12872_025_04550_w crossref_primary_10_1109_TEVC_2023_3238420 crossref_primary_10_1093_nsr_nwad125 crossref_primary_10_1002_clen_70039 crossref_primary_10_1007_s10115_025_02340_6 crossref_primary_10_1007_s42452_024_06440_w crossref_primary_10_4018_IJSSMET_2020070104 crossref_primary_10_3390_machines10020072 crossref_primary_10_3390_en15103485 crossref_primary_10_1016_j_compbiomed_2024_109590 crossref_primary_10_1109_ACCESS_2021_3094735 crossref_primary_10_1016_j_artmed_2024_102991 crossref_primary_10_1080_10106049_2020_1831622 crossref_primary_10_1016_j_eswa_2025_127535 crossref_primary_10_1038_s41386_025_02176_2 crossref_primary_10_1007_s41060_024_00668_w crossref_primary_10_1016_j_jksuci_2020_05_002 crossref_primary_10_1007_s13042_019_00968_9 crossref_primary_10_1016_j_ipm_2023_103370 crossref_primary_10_3233_IDA_226551 crossref_primary_10_1093_tse_tdad014 crossref_primary_10_1109_ACCESS_2020_2985414 crossref_primary_10_1162_netn_a_00447 crossref_primary_10_1016_j_oceaneng_2025_122713 crossref_primary_10_1109_ACCESS_2024_3467049 crossref_primary_10_3389_fmolb_2023_1094321 crossref_primary_10_3390_computers12100195 crossref_primary_10_3390_app14051960 crossref_primary_10_1155_2020_1958149 crossref_primary_10_1016_j_compstruct_2019_111698 crossref_primary_10_1093_jcde_qwac119 crossref_primary_10_1016_j_ijepes_2025_110526 crossref_primary_10_1016_j_asoc_2023_110959 crossref_primary_10_1016_j_ipl_2023_106407 crossref_primary_10_1016_j_neucom_2022_11_023 crossref_primary_10_3390_app15073718 crossref_primary_10_1016_j_ins_2022_01_008 crossref_primary_10_1007_s10666_025_10061_x crossref_primary_10_1186_s12864_024_10668_6 crossref_primary_10_1063_5_0228503 crossref_primary_10_3390_rs15112826 crossref_primary_10_1016_j_neucom_2022_04_083 crossref_primary_10_3390_app11062458 crossref_primary_10_1155_2024_4853773 crossref_primary_10_3390_app13148511 crossref_primary_10_1002_cam4_5420 crossref_primary_10_1186_s12911_023_02154_y crossref_primary_10_3390_axioms11050223 crossref_primary_10_3390_toxics11120955 crossref_primary_10_1016_j_ins_2018_12_033 crossref_primary_10_1109_ACCESS_2020_2976947 crossref_primary_10_1080_24751448_2024_2313443 crossref_primary_10_1007_s11276_024_03736_y crossref_primary_10_3390_agronomy14122940 crossref_primary_10_1007_s40747_021_00384_z crossref_primary_10_1016_j_fuel_2024_131694 crossref_primary_10_1371_journal_pone_0280648 crossref_primary_10_1016_j_jasrep_2025_105107 crossref_primary_10_1186_s12911_025_03116_2 crossref_primary_10_3390_app11041742 crossref_primary_10_1007_s00226_020_01184_3 crossref_primary_10_1080_09537287_2022_2157776 crossref_primary_10_1186_s13036_022_00319_3 crossref_primary_10_1109_TUFFC_2023_3278494 crossref_primary_10_1007_s00521_021_06125_0 crossref_primary_10_3390_electronics10151818 crossref_primary_10_1016_j_knosys_2023_110640 crossref_primary_10_1016_j_jhydrol_2023_130141 crossref_primary_10_1080_15376516_2024_2387733 crossref_primary_10_1080_00140139_2024_2430370 crossref_primary_10_1016_j_ins_2023_119910 crossref_primary_10_1109_TC_2025_3576941 crossref_primary_10_1109_ACCESS_2021_3116201 crossref_primary_10_3390_rs13132592 crossref_primary_10_1177_20552076241272739 crossref_primary_10_52294_001c_117311 crossref_primary_10_1016_j_autcon_2022_104714 crossref_primary_10_1007_s10489_024_05288_2 crossref_primary_10_1016_j_enbuild_2024_115007 crossref_primary_10_1038_s41598_025_95019_5 crossref_primary_10_1109_ACCESS_2025_3546720 crossref_primary_10_1016_j_heares_2022_108667 crossref_primary_10_1016_j_eswa_2024_125084 crossref_primary_10_3390_info15100625 crossref_primary_10_1016_j_ifacol_2020_12_2855 crossref_primary_10_1016_j_neucom_2022_11_013 crossref_primary_10_1007_s11063_023_11332_y crossref_primary_10_1108_SASBE_02_2024_0059 crossref_primary_10_3389_fmicb_2021_694534 crossref_primary_10_1186_s12911_023_02167_7 crossref_primary_10_3390_e25020325 crossref_primary_10_1155_2022_3397972 crossref_primary_10_1016_j_seppur_2025_134358 crossref_primary_10_1007_s13042_025_02781_z crossref_primary_10_1016_j_eswa_2021_115224 crossref_primary_10_1016_j_buildenv_2023_110157 crossref_primary_10_3390_agriculture10090400 crossref_primary_10_7717_peerj_cs_944 crossref_primary_10_1007_s11063_023_11256_7 crossref_primary_10_1007_s12008_022_01006_9 crossref_primary_10_1007_s10115_020_01519_3 crossref_primary_10_1109_TED_2023_3288842 crossref_primary_10_3389_fpsyg_2024_1447968 crossref_primary_10_1016_j_neucom_2018_09_077 crossref_primary_10_1186_s13634_022_00939_3 crossref_primary_10_3390_sym14101955 crossref_primary_10_1007_s12065_019_00293_8 crossref_primary_10_1016_j_compag_2025_110213 crossref_primary_10_3390_ijms25189844 crossref_primary_10_1007_s10661_024_13506_0 crossref_primary_10_3390_genes15050603 crossref_primary_10_1016_j_apenergy_2023_122079 crossref_primary_10_1109_TNNLS_2021_3131234 crossref_primary_10_1016_j_eswa_2022_117923 crossref_primary_10_1016_j_jnca_2022_103464 crossref_primary_10_1016_j_asoc_2023_110765 crossref_primary_10_1016_j_knosys_2023_110697 crossref_primary_10_1016_j_precisioneng_2024_07_006 crossref_primary_10_3233_JIFS_200653 crossref_primary_10_1007_s11270_024_06913_z crossref_primary_10_1016_j_cja_2024_10_019 crossref_primary_10_1016_j_renene_2023_118994 crossref_primary_10_1038_s43856_021_00020_4 crossref_primary_10_1016_j_eswa_2023_120152 crossref_primary_10_1016_j_commatsci_2024_113423 crossref_primary_10_1080_19942060_2021_1972043 crossref_primary_10_3390_s23010098 crossref_primary_10_3390_atmos13111832 crossref_primary_10_1002_cpe_7282 crossref_primary_10_1016_j_patcog_2024_110436 crossref_primary_10_1111_nph_18053 crossref_primary_10_1364_JOCN_560757 crossref_primary_10_1109_TIM_2022_3193407 crossref_primary_10_1186_s40537_021_00415_z crossref_primary_10_1007_s13762_019_02485_2 crossref_primary_10_1016_j_eswa_2022_116822 crossref_primary_10_1016_j_afres_2025_100767 crossref_primary_10_1109_TGRS_2023_3284475 crossref_primary_10_3389_fphar_2020_606668 crossref_primary_10_1007_s12145_025_01841_w crossref_primary_10_1108_TQM_09_2023_0302 crossref_primary_10_3390_sym11111423 crossref_primary_10_1016_j_adhoc_2023_103180 crossref_primary_10_1109_ACCESS_2019_2948159 crossref_primary_10_3233_JIFS_222401 crossref_primary_10_1007_s11356_023_27382_7 crossref_primary_10_1108_K_12_2022_1676 crossref_primary_10_1016_j_eswa_2021_115678 crossref_primary_10_1080_1062936X_2024_2378797 crossref_primary_10_1016_j_ijar_2021_09_016 crossref_primary_10_3390_inventions7010015 crossref_primary_10_1016_j_bspc_2022_104417 crossref_primary_10_1007_s11665_025_11096_7 crossref_primary_10_1177_00332941251343544 crossref_primary_10_1155_2021_9947059 crossref_primary_10_1038_s41598_024_53170_5 crossref_primary_10_1017_dce_2020_14 crossref_primary_10_1109_TNSRE_2021_3113888 crossref_primary_10_1007_s00521_022_07254_w crossref_primary_10_1016_j_measurement_2020_108461 crossref_primary_10_1016_j_algal_2025_104155 crossref_primary_10_1080_23311916_2025_2467153 crossref_primary_10_1186_s42774_021_00088_5 crossref_primary_10_1007_s10853_025_11197_7 crossref_primary_10_2139_ssrn_3788349 crossref_primary_10_1016_j_eswa_2023_121398 crossref_primary_10_35234_fumbd_1501652 crossref_primary_10_1094_PHYTO_05_20_0185_R crossref_primary_10_1177_20552076251333660 crossref_primary_10_1186_s12958_024_01285_9 crossref_primary_10_3390_s24010155 crossref_primary_10_1007_s12145_024_01592_0 crossref_primary_10_3390_rs14020378 crossref_primary_10_3390_rs16122229 crossref_primary_10_1080_15366367_2023_2246111 crossref_primary_10_1109_TMI_2022_3177626 crossref_primary_10_1007_s41060_020_00234_0 crossref_primary_10_1007_s13042_024_02108_4 crossref_primary_10_3389_fninf_2021_663592 crossref_primary_10_1007_s13346_023_01510_9 crossref_primary_10_1007_s11356_024_32807_y crossref_primary_10_1016_j_comnet_2023_110093 crossref_primary_10_1016_j_eswa_2021_115130 crossref_primary_10_1080_10106049_2023_2252781 crossref_primary_10_1016_j_asoc_2019_105924 crossref_primary_10_1016_j_ibmed_2025_100199 crossref_primary_10_1155_2024_5653690 crossref_primary_10_1016_j_impact_2025_100563 crossref_primary_10_1016_j_procs_2024_08_027 crossref_primary_10_1016_j_jaap_2021_105352 crossref_primary_10_1038_s41598_024_56820_w crossref_primary_10_3390_atmos13101672 crossref_primary_10_3390_electronics13122242 crossref_primary_10_1016_j_aei_2024_102852 crossref_primary_10_1155_2021_1676197 crossref_primary_10_1186_s12911_022_02051_w crossref_primary_10_1016_j_asoc_2019_105936 crossref_primary_10_1016_j_knosys_2021_107418 crossref_primary_10_3390_sym13101764 crossref_primary_10_1007_s11042_023_17724_5 crossref_primary_10_1080_10298436_2021_2001814 crossref_primary_10_1088_1742_6596_2273_1_012002 crossref_primary_10_1016_j_neucom_2022_10_053 crossref_primary_10_1186_s12872_025_04786_6 crossref_primary_10_3389_fimmu_2022_1027631 crossref_primary_10_1016_j_ecoinf_2024_102768 crossref_primary_10_1016_j_vascn_2020_106894 crossref_primary_10_1109_LCSYS_2020_3006428 crossref_primary_10_3390_metabo13121204 crossref_primary_10_3390_electronics11010114 crossref_primary_10_1016_j_ymssp_2023_110796 crossref_primary_10_1002_widm_70043 crossref_primary_10_1155_2021_8783899 crossref_primary_10_1088_1755_1315_1274_1_012008 crossref_primary_10_1016_j_csda_2019_106839 crossref_primary_10_1007_s10796_023_10431_4 crossref_primary_10_1007_s42979_025_03982_7 crossref_primary_10_1016_j_asoc_2020_106552 crossref_primary_10_1007_s10115_023_02010_5 crossref_primary_10_1016_j_ijrefrig_2024_01_006 crossref_primary_10_1109_TCYB_2021_3087776 crossref_primary_10_3390_f13020160 crossref_primary_10_3233_THC_230161 crossref_primary_10_1016_j_rcim_2021_102281 crossref_primary_10_1016_j_jece_2025_117826 crossref_primary_10_1155_2020_2350627 crossref_primary_10_1155_2021_5046244 crossref_primary_10_3389_fonc_2022_819673 crossref_primary_10_1016_j_apenergy_2024_123314 crossref_primary_10_1016_j_measurement_2025_117785 crossref_primary_10_1007_s13042_020_01180_w crossref_primary_10_1109_ACCESS_2022_3182543 crossref_primary_10_1007_s11227_023_05290_4 crossref_primary_10_1108_IJOA_03_2021_2684 crossref_primary_10_1016_j_asoc_2021_107993 crossref_primary_10_1007_s11227_023_05444_4 crossref_primary_10_3390_math10224268 crossref_primary_10_1016_j_patrec_2021_04_025 crossref_primary_10_1109_JBHI_2022_3212475 crossref_primary_10_1007_s13369_021_05486_x crossref_primary_10_1155_2022_9113683 crossref_primary_10_1007_s00521_024_10350_8 crossref_primary_10_3390_app9204257 crossref_primary_10_1109_JBHI_2022_3212479 crossref_primary_10_3390_buildings12101505 crossref_primary_10_1016_j_jmapro_2025_07_083 crossref_primary_10_1155_2021_5554873 crossref_primary_10_1016_j_ins_2019_06_063 crossref_primary_10_1186_s12989_023_00530_0 crossref_primary_10_3389_fnhum_2023_1205881 crossref_primary_10_1016_j_imavis_2024_105064 crossref_primary_10_14778_3611540_3611550 crossref_primary_10_1016_j_coal_2022_104054 crossref_primary_10_3390_e21040346 crossref_primary_10_1016_j_jretconser_2023_103513 crossref_primary_10_1089_met_2019_0104 crossref_primary_10_1177_07316844241236696 crossref_primary_10_3390_data6080080 crossref_primary_10_1016_j_csbj_2022_03_019 crossref_primary_10_1016_j_ecoinf_2024_102954 crossref_primary_10_1007_s12145_025_01938_2 crossref_primary_10_1007_s11063_021_10479_w crossref_primary_10_1016_j_jweia_2022_104904 crossref_primary_10_1108_JEIM_01_2019_0017 crossref_primary_10_3390_fi16100368 crossref_primary_10_3390_ijms21062114 crossref_primary_10_3390_met14040400 crossref_primary_10_1080_08839514_2021_2001178 crossref_primary_10_1016_j_asr_2023_07_064 crossref_primary_10_1016_j_knosys_2021_107633 crossref_primary_10_1109_JPROC_2020_2977054 crossref_primary_10_1155_2022_5191929 crossref_primary_10_3389_fenvs_2021_809995 crossref_primary_10_3390_diagnostics13081506 crossref_primary_10_3390_bs12050128 crossref_primary_10_3390_atmos13091412 crossref_primary_10_1016_j_ins_2024_120277 crossref_primary_10_3390_computers13120328 crossref_primary_10_1016_j_scitotenv_2024_176918 crossref_primary_10_1007_s11356_023_26362_1 crossref_primary_10_1109_TNSM_2023_3249470 crossref_primary_10_1002_minf_202300210 crossref_primary_10_1007_s00500_020_04877_w crossref_primary_10_3390_land12071287 crossref_primary_10_3390_sym14091916 crossref_primary_10_1093_pnasnexus_pgac039 crossref_primary_10_1155_2022_2941840 crossref_primary_10_1016_j_conbuildmat_2022_130066 crossref_primary_10_1016_j_energy_2025_134781 crossref_primary_10_1016_j_comnet_2023_110005 crossref_primary_10_1007_s13735_021_00210_9 crossref_primary_10_1371_journal_pone_0310840 crossref_primary_10_1007_s13246_025_01593_3 crossref_primary_10_3390_en15239053 crossref_primary_10_1016_j_ins_2021_12_047 crossref_primary_10_1109_ACCESS_2024_3507280 crossref_primary_10_1016_j_apr_2025_102456 crossref_primary_10_1016_j_inffus_2019_09_001 crossref_primary_10_1371_journal_pone_0269195 crossref_primary_10_1016_j_engappai_2024_109510 crossref_primary_10_1038_s41598_025_97938_9 crossref_primary_10_1088_1742_6596_1192_1_012004 crossref_primary_10_1007_s00521_023_08960_9 crossref_primary_10_3390_rs10081319 crossref_primary_10_3390_s23094178 crossref_primary_10_1007_s10489_025_06637_5 crossref_primary_10_1007_s12539_021_00422_x crossref_primary_10_1016_j_eswa_2024_124568 crossref_primary_10_1051_matecconf_202133607011 crossref_primary_10_1007_s00521_022_07067_x crossref_primary_10_1016_j_jag_2023_103386 crossref_primary_10_1038_s41598_022_16417_7 crossref_primary_10_1155_2022_6283618 crossref_primary_10_1021_acsami_5c09626 crossref_primary_10_1007_s12613_019_1724_x crossref_primary_10_1053_j_sodo_2021_05_006 crossref_primary_10_1109_ACCESS_2025_3569271 crossref_primary_10_1186_s12882_023_03084_7 crossref_primary_10_32604_cmes_2023_022699 crossref_primary_10_1109_TFUZZ_2024_3488074 crossref_primary_10_1016_j_compag_2023_108388 crossref_primary_10_3390_rs13050859 crossref_primary_10_1155_2024_5589891 crossref_primary_10_1007_s11814_023_1502_3 crossref_primary_10_1186_s12859_023_05502_x crossref_primary_10_1007_s11768_021_00077_5 crossref_primary_10_1109_ACCESS_2020_3040980 crossref_primary_10_1186_s12911_023_02192_6 crossref_primary_10_3390_electronics12194158 crossref_primary_10_1007_s13198_024_02327_6 crossref_primary_10_1016_j_saa_2024_125452 crossref_primary_10_3390_met13101641 crossref_primary_10_1109_TNNLS_2021_3105585 crossref_primary_10_1016_j_techfore_2021_121275 crossref_primary_10_1007_s00521_021_05846_6 crossref_primary_10_1016_j_eswa_2021_115191 crossref_primary_10_1038_s41598_023_50742_9 crossref_primary_10_1109_ACCESS_2024_3512878 crossref_primary_10_1038_s41598_020_59981_6 crossref_primary_10_1111_exsy_12925 crossref_primary_10_1007_s11227_023_05163_w crossref_primary_10_1016_j_knosys_2022_109749 crossref_primary_10_1007_s10462_019_09682_y crossref_primary_10_1016_j_neucom_2021_12_022 crossref_primary_10_1016_j_neucom_2022_09_009 crossref_primary_10_1016_j_rechem_2025_102730 crossref_primary_10_1016_j_jallcom_2025_182197 crossref_primary_10_1016_j_cose_2025_104478 crossref_primary_10_1016_j_scitotenv_2022_158271 crossref_primary_10_1016_j_ecss_2022_108053 crossref_primary_10_1007_s00500_025_10751_4 crossref_primary_10_1016_j_datak_2023_102176 crossref_primary_10_1109_TGRS_2021_3125323 crossref_primary_10_1016_j_asoc_2022_108964 crossref_primary_10_1007_s13369_024_09183_3 crossref_primary_10_1016_j_eswa_2024_123245 crossref_primary_10_32604_cmc_2022_028055 crossref_primary_10_1007_s41064_024_00323_w crossref_primary_10_1016_j_jnca_2024_104034 crossref_primary_10_1007_s10916_023_01983_8 crossref_primary_10_1007_s43032_024_01655_z crossref_primary_10_3390_electronics10020179 crossref_primary_10_1016_j_enconman_2020_113206 crossref_primary_10_3390_app8122548 crossref_primary_10_1007_s00253_022_11963_6 crossref_primary_10_31799_1684_8853_2018_5_2_12 crossref_primary_10_1007_s10639_025_13370_6 crossref_primary_10_3390_e23081011 crossref_primary_10_1007_s11063_021_10557_z crossref_primary_10_1016_j_jobe_2022_105028 crossref_primary_10_1111_bmsp_12218 crossref_primary_10_1080_14498596_2023_2174196 crossref_primary_10_1002_brb3_70289 crossref_primary_10_1016_j_knosys_2021_107224 crossref_primary_10_1007_s00477_022_02276_1 crossref_primary_10_1016_j_knosys_2020_106097 crossref_primary_10_1016_j_measurement_2022_111177 crossref_primary_10_1016_j_ins_2023_02_068 crossref_primary_10_3390_ijerph18115820 crossref_primary_10_3390_rs15041096 crossref_primary_10_1186_s12859_020_03683_3 crossref_primary_10_1007_s10639_022_11221_2 crossref_primary_10_1016_j_irbm_2020_01_006 crossref_primary_10_1016_j_compbiomed_2023_107389 crossref_primary_10_1109_ACCESS_2024_3436088 crossref_primary_10_1016_j_comtox_2023_100266 crossref_primary_10_3233_JIFS_189540 crossref_primary_10_1007_s11831_021_09700_9 crossref_primary_10_1016_j_asoc_2022_108744 crossref_primary_10_1109_ACCESS_2024_3454516 crossref_primary_10_1016_j_petsci_2025_08_011 crossref_primary_10_3390_diagnostics13142323 crossref_primary_10_3390_e25091250 crossref_primary_10_1155_2022_4131058 crossref_primary_10_1007_s13042_022_01633_4 crossref_primary_10_1016_j_chemolab_2023_104807 crossref_primary_10_1016_j_compbiomed_2021_104527 crossref_primary_10_1186_s12911_025_03030_7 crossref_primary_10_1016_j_actatropica_2025_107657 crossref_primary_10_1016_j_frl_2023_104784 crossref_primary_10_1016_j_knosys_2024_111380 crossref_primary_10_1016_j_knosys_2023_111084 crossref_primary_10_1016_j_ress_2019_106706 crossref_primary_10_1155_er_8022398 crossref_primary_10_1016_j_gecco_2025_e03630 crossref_primary_10_1093_schbul_sbae110 crossref_primary_10_1177_20552076231173225 crossref_primary_10_1016_j_conengprac_2020_104673 crossref_primary_10_14358_PERS_21_00006R2 |
| Cites_doi | 10.1109/TST.2015.7297748 10.1016/j.patrec.2012.03.017 10.1109/34.481557 10.1109/34.531802 10.1023/A:1010933404324 10.1016/j.neucom.2016.09.123 10.1039/C2MB25327E 10.1016/j.patcog.2009.12.013 10.1093/bioinformatics/btq381 10.1109/TCBB.2013.10 10.1007/s00521-013-1368-0 10.1109/TNN.2010.2047114 10.1016/j.ins.2003.03.019 10.1142/S0218001499000604 10.1109/TPAMI.2005.159 10.1016/j.patrec.2009.10.013 10.1016/j.knosys.2015.05.014 10.3233/ISB-00132 10.1016/S0167-8655(02)00196-4 10.1016/j.ymeth.2016.08.014 10.1109/34.990133 10.1016/j.neucom.2012.10.028 10.1016/j.neucom.2011.03.043 10.1016/j.patcog.2016.11.003 10.1109/TKDE.2013.86 10.1016/j.eswa.2010.12.134 10.1186/1471-2105-13-178 10.1016/j.asoc.2013.09.018 10.1016/j.ins.2011.09.027 10.1016/j.neucom.2007.06.014 10.1016/j.patcog.2010.02.008 10.1109/TCBB.2005.17 10.1016/j.neucom.2017.02.057 10.1016/j.patcog.2014.04.001 10.1198/jasa.2010.tm09415 10.1016/j.neunet.2011.12.002 10.1109/TKDE.2005.66 10.1109/TNN.2008.2004370 10.1016/j.neucom.2012.05.031 10.1016/0031-3203(90)90005-6 10.1016/S0959-1524(03)00029-5 10.1016/j.patcog.2014.06.002 10.1093/biostatistics/kxh010 10.1109/72.298224 10.1109/TKDE.2011.181 10.1016/j.procs.2010.04.273 10.1111/j.1467-9868.2005.00503.x 10.1016/j.patcog.2013.04.021 10.1016/j.eswa.2013.05.051 10.1109/TPAMI.2012.197 10.1109/TCBB.2012.33 10.1016/j.patcog.2007.10.009 10.1126/science.1242072 10.1093/bib/bbs006 10.1109/TIE.2014.2327589 10.1016/j.asoc.2014.01.018 10.1198/016214506000000735 10.1109/TPAMI.1984.4767500 10.1109/TCBB.2015.2478454 10.1016/j.eswa.2011.12.003 10.1016/j.knosys.2008.03.051 10.1016/j.patcog.2011.06.006 10.1016/j.patcog.2008.05.027 10.1111/j.2517-6161.1996.tb02080.x 10.1016/j.neucom.2012.06.036 10.1016/j.dsp.2004.12.004 10.1109/TPAMI.2008.77 10.1016/S0004-3702(97)00063-5 10.1016/j.knosys.2012.10.011 10.1109/LGRS.2015.2475299 10.1007/s12543-010-0047-4 10.1109/TIP.2010.2093906 10.1016/j.neucom.2014.12.123 10.1109/LGRS.2005.844169 10.1016/j.jnca.2011.01.002 10.1214/009053604000000067 10.1023/A:1007567018844 10.1023/A:1012487302797 10.1109/TKDE.2010.263 10.1109/34.709601 10.1093/bioinformatics/btl190 10.1016/j.csda.2009.12.013 10.1016/j.compbiolchem.2007.09.005 10.1016/j.procs.2013.05.011 10.1093/bioinformatics/btp630 10.1007/s10115-010-0288-x 10.1016/j.csda.2006.12.043 10.1109/TITB.2009.2018115 10.1016/j.patcog.2016.08.011 10.1109/TCBB.2005.28 10.1016/j.patrec.2010.12.014 10.1016/j.ipm.2004.08.006 |
| ContentType | Journal Article |
| Copyright | 2018 Elsevier B.V. |
| Copyright_xml | – notice: 2018 Elsevier B.V. |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.neucom.2017.11.077 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1872-8286 |
| EndPage | 79 |
| ExternalDocumentID | 10_1016_j_neucom_2017_11_077 S0925231218302911 |
| GroupedDBID | --- --K --M .DC .~1 0R~ 123 1B1 1~. 1~5 4.4 457 4G. 53G 5VS 7-5 71M 8P~ 9JM 9JN AABNK AACTN AADPK AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAXLA AAXUO AAYFN ABBOA ABCQJ ABFNM ABJNI ABMAC ABYKQ ACDAQ ACGFS ACRLP ACZNC ADBBV ADEZE AEBSH AEKER AENEX AFKWA AFTJW AFXIZ AGHFR AGUBO AGWIK AGYEJ AHHHB AHZHX AIALX AIEXJ AIKHN AITUG AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD AXJTR BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ IHE J1W KOM LG9 M41 MO0 MOBAO N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 RIG ROL RPZ SDF SDG SDP SES SPC SPCBC SSN SSV SSZ T5K ZMT ~G- 29N 9DU AAQXK AATTM AAXKI AAYWO AAYXX ABWVN ABXDB ACLOT ACNNM ACRPL ACVFH ADCNI ADJOM ADMUD ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP ASPBG AVWKF AZFZN CITATION EFKBS FEDTE FGOYB HLZ HVGLF HZ~ R2- SBC SEW WUQ XPP ~HD |
| ID | FETCH-LOGICAL-c372t-421a6cd7c50b8369cfbf54674a0465d34ef85c10a076e4cdda9e38526eac87d33 |
| ISICitedReferencesCount | 1373 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000432490900005&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0925-2312 |
| IngestDate | Sat Nov 29 07:14:51 EST 2025 Tue Nov 18 19:44:09 EST 2025 Fri Feb 23 02:30:25 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Dimensionality reduction Feature selection Data mining Machine learning |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c372t-421a6cd7c50b8369cfbf54674a0465d34ef85c10a076e4cdda9e38526eac87d33 |
| PageCount | 10 |
| ParticipantIDs | crossref_primary_10_1016_j_neucom_2017_11_077 crossref_citationtrail_10_1016_j_neucom_2017_11_077 elsevier_sciencedirect_doi_10_1016_j_neucom_2017_11_077 |
| PublicationCentury | 2000 |
| PublicationDate | 2018-07-26 |
| PublicationDateYYYYMMDD | 2018-07-26 |
| PublicationDate_xml | – month: 07 year: 2018 text: 2018-07-26 day: 26 |
| PublicationDecade | 2010 |
| PublicationTitle | Neurocomputing (Amsterdam) |
| PublicationYear | 2018 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Lu, Zhao, Zhang (bib0105) 2008; 21 Sotoca, Pla (bib0096) 2010; 43 Novovičová, Somol, Haindl, Pudil (bib0082) 2007 Swets, Weng (bib0015) 1995 Doquire, Verleysen (bib0129) 2011 Devaney, Ram (bib0122) 1997 Lane, Xue, Liu, Zhang (bib0038) 2014 Gibert, Valveny, Bunke (bib0037) 2012; 33 Vergara, Estévez (bib0068) 2014; 24 Li, Zheng, Huang (bib0048) 2008; 41 Doquire, Verleysen (bib0130) 2013; 121 De Bock, Coussement, Van den Poel (bib0156) 2010; 54 Abeel, Helleputte, Van de Peer, Dupont, Saeys (bib0160) 2010; 26 Herman, Zhang, Wang, Ye, Chen (bib0084) 2013; 46 Bonev (bib0045) 2010 Peng, Long, Ding (bib0042) 2005; 27 Langley (bib0006) 1996 Chen, Miao, Wang (bib0050) 2010; 31 Kononenko (bib0075) 1994 Fleuret (bib0043) 2004; 5 Huang (bib0063) 1996 Z. Zhao, F. Morstatter, S. Sharma, S. Alelyani, A. Anand, H. Liu, Advancing Feature Selection Research, ASU Feature Selection Repository (2010) 1–28. Álvarez-Estévez, Sánchez-Maroño, Alonso-Betanzos, Moret-Bonillo (bib0161) 2011; 38 Vasconcelos (bib0011) 2003 Gennari (bib0121) 1991 Bishop (bib0126) 1995 Wang, Yu (bib0032) 2005; 3612 Saeys, Abeel, Van de Peer (bib0151) 2008 Van Landeghem, Abeel, Saeys, Van de Peer (bib0020) 2010; 26 Liu, Han, Li, Wong (bib0025) 2004; 4 Battiti (bib0080) 1994; 5 Choi, Ro, Plataniotis (bib0013) 2011; 20 Tibshirani (bib0057) 1996; 58 Wang, Li, Fang (bib0158) 2012; 13 Zou, Zeng, Cao, Ji (bib0097) 2016; 173 Chiang, Pell (bib0104) 2004; 14 Mirkin (bib0119) 1999; 35 Morgan (bib0041) 2001; 57 Ahn, Moon, Fazzari, Lim, Chen, Kodell (bib0154) 2007; 51 El Akadi, Amine, El Ouardighi, Aboutajdine (bib0108) 2011; 26 Cadenas, Garrido, MartíNez (bib0109) 2013; 40 Zhang, Zhang, Lok, Lyu (bib0065) 2007; 185 Shen, Bai (bib0040) 2006 Liu, Zhang, Xiao, Zhu, Zhao (bib0095) 2014 Zhang, Li, Scarf, Ball (bib0034) 2011; 74 Song, Smola, Gretton, Borgwardt, Bedo (bib0077) 2007 Kalakech, Biela, Macaire, Hamad (bib0137) 2011; 32 Rashedi, Nezamabadi-Pour, Saryazdi (bib0017) 2013; 39 Zhao, Lu, He (bib0128) 2008; 71 Liu, Motoda (bib0002) 2012 Song, Ni, Wang (bib0026) 2013; 25 Furlanello, Serafini, Merler, Jurman (bib0099) 2005; 2 Lazar, Taminau, Meganck, Steenhoff, Coletta, Molter, de Schaetzen, Duque, Bersini, Nowe (bib0152) 2012; 9 Zhang, Suganthan (bib0159) 2014; 47 Opitz (bib0150) 1999 Jie, Zhang, Cheng, Shen (bib0047) 2013 Yu, Huang (bib0031) 2013; 10 Amiri, Yousefi, Lucas, Shakery, Yazdani (bib0022) 2011; 34 Guyon, Elisseeff (bib0003) 2003; 3 Alelyani, Tang, Liu (bib0067) 2013; 29 Zhang, Li, Wang, Zhang (bib0088) 2013; 101 Vaithyanathan, Dom (bib0123) 1999 Crowley, Parker (bib0007) 1984; 6 Derrac, Cornelis, García, Herrera (bib0052) 2012; 186 Hall (bib0073) 2000 Lin, Chen (bib0149) 2013; 14 Hastie, Tibshirani (bib0146) 2004; 5 Bolón-Canedo, Sánchez-Maroño, Alonso-Betanzos (bib0162) 2012; 45 Zhong, Wang, Peng, Zhang, Li (bib0100) 2015; 20 Wu, Yu, Ding, Wang, Zhu (bib0166) 2013; 35 Alazab, Hobbs, Abawajy, Alazab (bib0023) 2012 Dash, Liu (bib0110) 1999 Padungweang, Lursinsap, Sunat (bib0115) 2009 Yang, Moody (bib0044) 1999; 12 Zhou, Chan (bib0114) 2015 Japkowicz (bib0148) 2000 Zheng, Huang, Zhang, Kong (bib0030) 2009; 13 Cheng, Deng, Fu, Wang, Qin (bib0127) 2011 Singh, Baranwal, Sevakula, Verma, Cui (bib0170) 2016 Huang, Du (bib0064) 2008; 19 Agrawal, Gehrke, Gunopulos, Raghavan (bib0118) 1998 Li, Chen, Wasserman (bib0169) 2015 Zhang, Zhang (bib0083) 2012; 39 Lee, Lee (bib0087) 2006; 42 Liu, Liu, Zhang (bib0157) 2010; 43 Swiniarski, Skowron (bib0049) 2003; 24 Yu, Wu, Ding, Pei (bib0167) 2014 Zou, Hastie (bib0058) 2005; 67 Cheng, Qin, Qian, Liu (bib0085) 2008 Guyon, Weston, Barnhill, Vapnik (bib0098) 2002; 46 Huang (bib0060) 1999; 13 Stein, Chen, Wu, Hua (bib0021) 2005 Perkins, Theiler (bib0163) 2003 Tan, Tsang, Wang (bib0144) 2014; 15 Alibeigi, Hashemi, Hamzeh (bib0112) 2011; 2 Huang, Xu, Ng, Ye (bib0124) 2008 Yang, Hou, Wu (bib0134) 2011 Ruangkanokmas, Achalakul, Akkarajitsakul (bib0168) 2016 Antoniades, Took (bib0171) 2016 Weinberger, Dasgupta, Langford, Smola, Attenberg (bib0145) 2009 Shu, Shen (bib0051) 2014; 47 Yang, He, Qu, Zhang (bib0142) 2016 Rauber, de Assis Boldt, Varejão (bib0033) 2015; 62 Au, Chan, Wong, Wang (bib0094) 2005; 2 Saxena, Pal, Vora (bib0117) 2010; 2 Quinlan (bib0054) 2014 Dhillon, Mallela, Kumar (bib0089) 2003; 3 Wang, Guo, Wang (bib0053) 2010; 1 Khotanzad, Hong (bib0010) 1990; 23 Chuang, Chang, Tu, Yang (bib0106) 2008; 32 Ho (bib0153) 1998; 20 Yu, Liu (bib0079) 2004; 5 Liu, Li, Wong (bib0024) 2002; 13 Li, Li, Wu, Sun (bib0039) 2014; 19 Liu, Yu (bib0078) 2005; 17 Zou (bib0059) 2006; 101 Mitra, Murthy, Pal (bib0113) 2002; 24 Khoshgoftaar, Dittman, Wald, Fazelpour (bib0036) 2012 Novovicová, Pudil, Kittler (bib0086) 1996; 18 Kwak, Choi (bib0081) 1999 Xue, Zhang, Browne (bib0107) 2014; 18 Gao, Li, Cai, Feng, Li, Jiang (bib0028) 2013; 9 Mirzaei, Mohsenzadeh, Sheikhzadeh (bib0056) 2017; 241 Jing, Huang, Shi (bib0019) 2002 Huang, Zheng (bib0029) 2006; 22 Ienco, Meo (bib0090) 2008 Yang, Chen, Ji (bib0132) 2010 Dy, Brodley (bib0120) 2004; 5 Benabdeslem, Hindawi (bib0138) 2011 Blum, Langley (bib0001) 1997; 97 Witten, Tibshirani (bib0091) 2010; 105 Sun, Huang, Cheun (bib0008) 2005; 15 Sunzhong, Jiang, Zhao, Wang, Fan (bib0133) 2013 Vasconcelos, Vasconcelos (bib0012) 2004 Li, Hu, Shen, Chen, Li (bib0027) 2008 Bolón-Canedo, Sánchez-Maroño, Alonso-Betanzos (bib0069) 2015; 86 Wang, Wang, Chang (bib0147) 2016; 111 Zou, Ni, Zhang, Wang (bib0172) 2015; 12 Swets, Weng (bib0016) 1996; 18 Zhou, Foster, Stine, Ungar (bib0164) 2006; 7 Han, Pei, Kamber (bib0062) 2011 Deng, Berg, Fei-Fei (bib0143) 2011 Goltsev, Gritsenko (bib0014) 2012; 28 Kira, Rendell (bib0074) 1992 Martín-Smith, Ortega, Asensio-Cubero, Gan, Ortiz (bib0076) 2017; 250 Benabdeslem, Hindawi (bib0140) 2014; 26 Chen, Huang, Huang (bib0131) 2010 Javed, Babri, Saeed (bib0102) 2012; 24 Deepthi, Thampi (bib0125) 2015 Xu, King, Lyu, Jin (bib0046) 2010; 21 Zhao, Deng, Shi (bib0093) 2013; 17 Efron, Hastie, Johnstone, Tibshirani (bib0055) 2004; 32 Sheikhpour, Sarram, Gharaghani, Chahooki (bib0071) 2017; 64 Vandenbroucke, Macaire, Postaire (bib0111) 2000 Zhang, Chen, Zhou (bib0139) 2008; 41 Sun, Huang, Cheung, Liu, Huang (bib0009) 2005; 2 Lewis, Yang, Rose, Li (bib0018) 2004; 5 Fayyad, Irani (bib0072) 1993 Liu, Nie, Wu, Chen (bib0136) 2010 Hsu (bib0103) 2004; 163 Wang, Wang, Liao, Chen (bib0141) 2017; 61 Tang, Alelyani, Liu (bib0066) 2014 Rodriguez, Laio (bib0061) 2014; 344 Langley (bib0005) 1994 Liu, Nie, Wu, Chen (bib0135) 2013; 105 He, Cai, Niyogi (bib0116) 2005 Wu, Yu, Wang, Ding (bib0165) 2010 Ang, Mirzal, Haron, Hamed (bib0070) 2016; 13 Michalak, Kwaśnicka (bib0101) 2006; 16 Liu, Wu, Zhang (bib0092) 2011 Breiman (bib0155) 2001; 45 Vasconcelos, Vasconcelos (bib0035) 2009; 31 Khoshgoftaar (10.1016/j.neucom.2017.11.077_bib0036) 2012 10.1016/j.neucom.2017.11.077_bib0004 Liu (10.1016/j.neucom.2017.11.077_bib0025) 2004; 4 Yu (10.1016/j.neucom.2017.11.077_bib0167) 2014 Novovičová (10.1016/j.neucom.2017.11.077_bib0082) 2007 Zhang (10.1016/j.neucom.2017.11.077_bib0034) 2011; 74 Lazar (10.1016/j.neucom.2017.11.077_bib0152) 2012; 9 Xu (10.1016/j.neucom.2017.11.077_bib0046) 2010; 21 He (10.1016/j.neucom.2017.11.077_bib0116) 2005 Huang (10.1016/j.neucom.2017.11.077_bib0124) 2008 Benabdeslem (10.1016/j.neucom.2017.11.077_bib0140) 2014; 26 Liu (10.1016/j.neucom.2017.11.077_bib0092) 2011 Goltsev (10.1016/j.neucom.2017.11.077_bib0014) 2012; 28 Au (10.1016/j.neucom.2017.11.077_bib0094) 2005; 2 Javed (10.1016/j.neucom.2017.11.077_bib0102) 2012; 24 Wang (10.1016/j.neucom.2017.11.077_bib0053) 2010; 1 Liu (10.1016/j.neucom.2017.11.077_bib0002) 2012 Tang (10.1016/j.neucom.2017.11.077_bib0066) 2014 Liu (10.1016/j.neucom.2017.11.077_bib0095) 2014 De Bock (10.1016/j.neucom.2017.11.077_bib0156) 2010; 54 Padungweang (10.1016/j.neucom.2017.11.077_bib0115) 2009 Wang (10.1016/j.neucom.2017.11.077_bib0158) 2012; 13 Kononenko (10.1016/j.neucom.2017.11.077_bib0075) 1994 Cadenas (10.1016/j.neucom.2017.11.077_bib0109) 2013; 40 Deepthi (10.1016/j.neucom.2017.11.077_bib0125) 2015 Fayyad (10.1016/j.neucom.2017.11.077_bib0072) 1993 Choi (10.1016/j.neucom.2017.11.077_bib0013) 2011; 20 Lee (10.1016/j.neucom.2017.11.077_bib0087) 2006; 42 Rauber (10.1016/j.neucom.2017.11.077_bib0033) 2015; 62 Liu (10.1016/j.neucom.2017.11.077_bib0078) 2005; 17 Wang (10.1016/j.neucom.2017.11.077_bib0147) 2016; 111 Bolón-Canedo (10.1016/j.neucom.2017.11.077_bib0162) 2012; 45 Li (10.1016/j.neucom.2017.11.077_bib0169) 2015 Song (10.1016/j.neucom.2017.11.077_bib0077) 2007 Guyon (10.1016/j.neucom.2017.11.077_bib0098) 2002; 46 Michalak (10.1016/j.neucom.2017.11.077_bib0101) 2006; 16 Wu (10.1016/j.neucom.2017.11.077_bib0165) 2010 Herman (10.1016/j.neucom.2017.11.077_bib0084) 2013; 46 Zhang (10.1016/j.neucom.2017.11.077_bib0088) 2013; 101 Cheng (10.1016/j.neucom.2017.11.077_bib0127) 2011 Zhang (10.1016/j.neucom.2017.11.077_bib0139) 2008; 41 Liu (10.1016/j.neucom.2017.11.077_bib0136) 2010 Benabdeslem (10.1016/j.neucom.2017.11.077_bib0138) 2011 Weinberger (10.1016/j.neucom.2017.11.077_bib0145) 2009 Fleuret (10.1016/j.neucom.2017.11.077_bib0043) 2004; 5 Yu (10.1016/j.neucom.2017.11.077_bib0031) 2013; 10 Sun (10.1016/j.neucom.2017.11.077_bib0009) 2005; 2 Dy (10.1016/j.neucom.2017.11.077_bib0120) 2004; 5 Bishop (10.1016/j.neucom.2017.11.077_bib0126) 1995 Zou (10.1016/j.neucom.2017.11.077_bib0058) 2005; 67 Zou (10.1016/j.neucom.2017.11.077_bib0059) 2006; 101 Shen (10.1016/j.neucom.2017.11.077_bib0040) 2006 Opitz (10.1016/j.neucom.2017.11.077_bib0150) 1999 Vasconcelos (10.1016/j.neucom.2017.11.077_bib0035) 2009; 31 Sun (10.1016/j.neucom.2017.11.077_bib0008) 2005; 15 Battiti (10.1016/j.neucom.2017.11.077_bib0080) 1994; 5 Khotanzad (10.1016/j.neucom.2017.11.077_bib0010) 1990; 23 Alibeigi (10.1016/j.neucom.2017.11.077_bib0112) 2011; 2 Zhou (10.1016/j.neucom.2017.11.077_bib0114) 2015 Devaney (10.1016/j.neucom.2017.11.077_bib0122) 1997 Perkins (10.1016/j.neucom.2017.11.077_bib0163) 2003 Zhang (10.1016/j.neucom.2017.11.077_bib0065) 2007; 185 Vasconcelos (10.1016/j.neucom.2017.11.077_bib0012) 2004 Mitra (10.1016/j.neucom.2017.11.077_bib0113) 2002; 24 Singh (10.1016/j.neucom.2017.11.077_bib0170) 2016 Dash (10.1016/j.neucom.2017.11.077_bib0110) 1999 Chen (10.1016/j.neucom.2017.11.077_bib0131) 2010 Peng (10.1016/j.neucom.2017.11.077_bib0042) 2005; 27 Yang (10.1016/j.neucom.2017.11.077_bib0134) 2011 Furlanello (10.1016/j.neucom.2017.11.077_bib0099) 2005; 2 Mirzaei (10.1016/j.neucom.2017.11.077_bib0056) 2017; 241 Zhao (10.1016/j.neucom.2017.11.077_bib0128) 2008; 71 Ho (10.1016/j.neucom.2017.11.077_bib0153) 1998; 20 Gao (10.1016/j.neucom.2017.11.077_bib0028) 2013; 9 Agrawal (10.1016/j.neucom.2017.11.077_bib0118) 1998 Alazab (10.1016/j.neucom.2017.11.077_bib0023) 2012 Liu (10.1016/j.neucom.2017.11.077_bib0024) 2002; 13 Huang (10.1016/j.neucom.2017.11.077_bib0060) 1999; 13 Breiman (10.1016/j.neucom.2017.11.077_bib0155) 2001; 45 Tibshirani (10.1016/j.neucom.2017.11.077_bib0057) 1996; 58 Martín-Smith (10.1016/j.neucom.2017.11.077_bib0076) 2017; 250 Tan (10.1016/j.neucom.2017.11.077_bib0144) 2014; 15 Langley (10.1016/j.neucom.2017.11.077_bib0005) 1994 Sheikhpour (10.1016/j.neucom.2017.11.077_bib0071) 2017; 64 Li (10.1016/j.neucom.2017.11.077_bib0039) 2014; 19 Japkowicz (10.1016/j.neucom.2017.11.077_bib0148) 2000 Sunzhong (10.1016/j.neucom.2017.11.077_bib0133) 2013 Vandenbroucke (10.1016/j.neucom.2017.11.077_bib0111) 2000 Zou (10.1016/j.neucom.2017.11.077_bib0097) 2016; 173 Ruangkanokmas (10.1016/j.neucom.2017.11.077_bib0168) 2016 Novovicová (10.1016/j.neucom.2017.11.077_bib0086) 1996; 18 Antoniades (10.1016/j.neucom.2017.11.077_bib0171) 2016 Zou (10.1016/j.neucom.2017.11.077_bib0172) 2015; 12 Han (10.1016/j.neucom.2017.11.077_bib0062) 2011 Chuang (10.1016/j.neucom.2017.11.077_bib0106) 2008; 32 Vasconcelos (10.1016/j.neucom.2017.11.077_bib0011) 2003 Swiniarski (10.1016/j.neucom.2017.11.077_bib0049) 2003; 24 Zhang (10.1016/j.neucom.2017.11.077_bib0083) 2012; 39 Blum (10.1016/j.neucom.2017.11.077_bib0001) 1997; 97 Chiang (10.1016/j.neucom.2017.11.077_bib0104) 2004; 14 Rodriguez (10.1016/j.neucom.2017.11.077_bib0061) 2014; 344 Vaithyanathan (10.1016/j.neucom.2017.11.077_bib0123) 1999 Huang (10.1016/j.neucom.2017.11.077_bib0063) 1996 Langley (10.1016/j.neucom.2017.11.077_bib0006) 1996 Swets (10.1016/j.neucom.2017.11.077_bib0016) 1996; 18 Kalakech (10.1016/j.neucom.2017.11.077_bib0137) 2011; 32 Song (10.1016/j.neucom.2017.11.077_bib0026) 2013; 25 Zhao (10.1016/j.neucom.2017.11.077_bib0093) 2013; 17 Wu (10.1016/j.neucom.2017.11.077_bib0166) 2013; 35 Chen (10.1016/j.neucom.2017.11.077_bib0050) 2010; 31 Yang (10.1016/j.neucom.2017.11.077_bib0044) 1999; 12 Wang (10.1016/j.neucom.2017.11.077_bib0032) 2005; 3612 Kira (10.1016/j.neucom.2017.11.077_bib0074) 1992 Álvarez-Estévez (10.1016/j.neucom.2017.11.077_bib0161) 2011; 38 Liu (10.1016/j.neucom.2017.11.077_bib0157) 2010; 43 Zhou (10.1016/j.neucom.2017.11.077_bib0164) 2006; 7 Crowley (10.1016/j.neucom.2017.11.077_bib0007) 1984; 6 Saxena (10.1016/j.neucom.2017.11.077_bib0117) 2010; 2 Yang (10.1016/j.neucom.2017.11.077_bib0142) 2016 Amiri (10.1016/j.neucom.2017.11.077_bib0022) 2011; 34 Li (10.1016/j.neucom.2017.11.077_bib0048) 2008; 41 Zhong (10.1016/j.neucom.2017.11.077_bib0100) 2015; 20 Zheng (10.1016/j.neucom.2017.11.077_bib0030) 2009; 13 Derrac (10.1016/j.neucom.2017.11.077_bib0052) 2012; 186 Doquire (10.1016/j.neucom.2017.11.077_bib0129) 2011 Mirkin (10.1016/j.neucom.2017.11.077_bib0119) 1999; 35 Lu (10.1016/j.neucom.2017.11.077_bib0105) 2008; 21 Efron (10.1016/j.neucom.2017.11.077_bib0055) 2004; 32 Ahn (10.1016/j.neucom.2017.11.077_bib0154) 2007; 51 Witten (10.1016/j.neucom.2017.11.077_bib0091) 2010; 105 El Akadi (10.1016/j.neucom.2017.11.077_bib0108) 2011; 26 Ang (10.1016/j.neucom.2017.11.077_bib0070) 2016; 13 Lane (10.1016/j.neucom.2017.11.077_bib0038) 2014 Gibert (10.1016/j.neucom.2017.11.077_bib0037) 2012; 33 Huang (10.1016/j.neucom.2017.11.077_bib0029) 2006; 22 Gennari (10.1016/j.neucom.2017.11.077_bib0121) 1991 Stein (10.1016/j.neucom.2017.11.077_bib0021) 2005 Vergara (10.1016/j.neucom.2017.11.077_bib0068) 2014; 24 Sotoca (10.1016/j.neucom.2017.11.077_bib0096) 2010; 43 Alelyani (10.1016/j.neucom.2017.11.077_bib0067) 2013; 29 Van Landeghem (10.1016/j.neucom.2017.11.077_bib0020) 2010; 26 Xue (10.1016/j.neucom.2017.11.077_bib0107) 2014; 18 Jing (10.1016/j.neucom.2017.11.077_bib0019) 2002 Li (10.1016/j.neucom.2017.11.077_bib0027) 2008 Hsu (10.1016/j.neucom.2017.11.077_bib0103) 2004; 163 Jie (10.1016/j.neucom.2017.11.077_bib0047) 2013 Lewis (10.1016/j.neucom.2017.11.077_bib0018) 2004; 5 Yang (10.1016/j.neucom.2017.11.077_bib0132) 2010 Yu (10.1016/j.neucom.2017.11.077_bib0079) 2004; 5 Liu (10.1016/j.neucom.2017.11.077_bib0135) 2013; 105 Rashedi (10.1016/j.neucom.2017.11.077_bib0017) 2013; 39 Dhillon (10.1016/j.neucom.2017.11.077_bib0089) 2003; 3 Morgan (10.1016/j.neucom.2017.11.077_bib0041) 2001; 57 Doquire (10.1016/j.neucom.2017.11.077_bib0130) 2013; 121 Saeys (10.1016/j.neucom.2017.11.077_bib0151) 2008 Lin (10.1016/j.neucom.2017.11.077_bib0149) 2013; 14 Cheng (10.1016/j.neucom.2017.11.077_bib0085) 2008 Bolón-Canedo (10.1016/j.neucom.2017.11.077_bib0069) 2015; 86 Hall (10.1016/j.neucom.2017.11.077_bib0073) 2000 Huang (10.1016/j.neucom.2017.11.077_bib0064) 2008; 19 Shu (10.1016/j.neucom.2017.11.077_bib0051) 2014; 47 Ienco (10.1016/j.neucom.2017.11.077_bib0090) 2008 Zhang (10.1016/j.neucom.2017.11.077_bib0159) 2014; 47 Guyon (10.1016/j.neucom.2017.11.077_bib0003) 2003; 3 Hastie (10.1016/j.neucom.2017.11.077_bib0146) 2004; 5 Swets (10.1016/j.neucom.2017.11.077_bib0015) 1995 Bonev (10.1016/j.neucom.2017.11.077_bib0045) 2010 Abeel (10.1016/j.neucom.2017.11.077_bib0160) 2010; 26 Deng (10.1016/j.neucom.2017.11.077_bib0143) 2011 Kwak (10.1016/j.neucom.2017.11.077_bib0081) 1999 Wang (10.1016/j.neucom.2017.11.077_bib0141) 2017; 61 Quinlan (10.1016/j.neucom.2017.11.077_bib0054) 2014 |
| References_xml | – volume: 9 start-page: 1106 year: 2012 end-page: 1119 ident: bib0152 article-title: A survey on filter techniques for feature selection in gene expression microarray analysis publication-title: IEEE/ACM Trans. Comput. Biol. Bioinf. – volume: 54 start-page: 1535 year: 2010 end-page: 1546 ident: bib0156 article-title: Ensemble classification based on generalized additive models publication-title: Comput. Stat. Data Anal. – start-page: 1 year: 2006 end-page: 11 ident: bib0040 article-title: Information theory for Gabor feature selection for face recognition publication-title: EURASIP J. Appl. Signal Process. – volume: 2 start-page: 110 year: 2005 end-page: 118 ident: bib0099 article-title: Semisupervised learning for molecular profiling publication-title: IEEE/ACM Trans. Comput. Biol. Bioinf. – start-page: 248 year: 2011 end-page: 255 ident: bib0129 article-title: Graph Laplacian for semi-supervised feature selection in regression problems publication-title: Proceedings of International Work-Conference on Artificial Neural Networks – volume: 185 start-page: 1026 year: 2007 end-page: 1037 ident: bib0065 article-title: A hybrid particle swarm optimization–back-propagation algorithm for feedforward neural network training publication-title: Appl. Math. Comput. – start-page: 41 year: 2008 end-page: 47 ident: bib0027 article-title: A novel unsupervised feature selection method for bioinformatics data sets through feature clustering publication-title: Proceedings of IEEE International Conference on Granular Computing – volume: 15 start-page: 1371 year: 2014 end-page: 1429 ident: bib0144 article-title: Towards ultrahigh dimensional feature selection for big data publication-title: J. Mach. Learn. Res. – volume: 35 start-page: 1178 year: 2013 end-page: 1192 ident: bib0166 article-title: Online feature selection with streaming features publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 71 start-page: 1842 year: 2008 end-page: 1849 ident: bib0128 article-title: Locality sensitive semi-supervised feature selection publication-title: Neurocomputing – start-page: 724 year: 1991 end-page: 728 ident: bib0121 article-title: Concept formation and attention publication-title: Proceedings of the Thirteenth Annual Conference of the Cognitive Science Society – volume: 7 start-page: 1861 year: 2006 end-page: 1885 ident: bib0164 article-title: Streamwise feature selection publication-title: J. Mach. Learn. Res. – start-page: 249 year: 1992 end-page: 256 ident: bib0074 article-title: A practical approach to feature selection publication-title: Proceedings of the Ninth International Workshop on Machine Learning – volume: 101 start-page: 32 year: 2013 end-page: 42 ident: bib0088 article-title: Divergence-based feature selection for separate classes publication-title: Neurocomputing – volume: 20 start-page: 832 year: 1998 end-page: 844 ident: bib0153 article-title: The random subspace method for constructing decision forests publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 1 start-page: 2425 year: 2010 end-page: 2432 ident: bib0053 article-title: Rough set and Tabu search based feature selection for credit scoring publication-title: Procedia Comput. Sci. – start-page: 171 year: 1994 end-page: 182 ident: bib0075 article-title: Estimating attributes: analysis and extensions of RELIEF publication-title: Proceedings of European Conference on Machine Learning – volume: 19 start-page: 57 year: 2014 end-page: 67 ident: bib0039 article-title: Statistics-based wrapper for feature selection: an implementation on financial distress identification with support vector machine publication-title: Appl. Soft Comput. – volume: 173 start-page: 346 year: 2016 end-page: 354 ident: bib0097 article-title: A novel features ranking metric with application to scalable visual and bioinformatics data classification publication-title: Neurocomputing – start-page: 379 year: 1999 end-page: 384 ident: bib0150 article-title: Feature selection for ensembles publication-title: Proceedings of 16th National Conference on Artificial Intelligence – start-page: 417 year: 2007 end-page: 426 ident: bib0082 article-title: Conditional mutual information based feature selection for classification task publication-title: Proceedings of the 12th Iberoamerican Conference on Congress on Pattern Recognition – start-page: 664 year: 2013 end-page: 668 ident: bib0133 article-title: Manifold based fisher method for semi-supervised feature selection publication-title: Proceedings of International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) – volume: 29 start-page: 110 year: 2013 end-page: 121 ident: bib0067 article-title: Feature selection for clustering: a revie publication-title: Data Clust.: Algorithms Appl. – volume: 12 start-page: 687 year: 1999 end-page: 693 ident: bib0044 article-title: Data visualization and feature selection: new algorithms for nongaussian data publication-title: Adv. Neural Inf. Process. Syst. – start-page: 1113 year: 2009 end-page: 1120 ident: bib0145 article-title: Feature hashing for large scale multitask learning publication-title: Proceedings of the 26th Annual International Conference on Machine Learning – volume: 74 start-page: 2941 year: 2011 end-page: 2952 ident: bib0034 article-title: Feature selection for high-dimensional machinery fault diagnosis data using multiple models and Radial Basis Function networks publication-title: Neurocomputing – volume: 18 start-page: 261 year: 2014 end-page: 276 ident: bib0107 article-title: Particle swarm optimisation for feature selection in classification: novel initialisation and updating mechanisms publication-title: Appl. Soft Comput. – volume: 24 start-page: 301 year: 2002 end-page: 312 ident: bib0113 article-title: Unsupervised feature selection using feature similarity publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 61 start-page: 511 year: 2017 end-page: 523 ident: bib0141 article-title: An efficient semi-supervised representatives feature selection algorithm based on information theory publication-title: Pattern Recognit. – volume: 47 start-page: 3429 year: 2014 end-page: 3437 ident: bib0159 article-title: Random forests with ensemble of feature spaces publication-title: Pattern Recognit. – volume: 105 start-page: 12 year: 2013 end-page: 18 ident: bib0135 article-title: Efficient semi-supervised feature selection with noise insensitive trace ratio criterion publication-title: Neurocomputing – start-page: 433 year: 1999 end-page: 443 ident: bib0123 article-title: Model selection in unsupervised learning with applications to document clustering publication-title: Proceedings of International Conference on Machine Learning – volume: 51 start-page: 6166 year: 2007 end-page: 6179 ident: bib0154 article-title: Classification by ensembles from random partitions of high-dimensional data publication-title: Comput. Stat. Data Anal. – start-page: 245 year: 1994 end-page: 271 ident: bib0005 article-title: Selection of relevant features in machine learning publication-title: Proceedings of the AAAI Fall Symposium on Relevance – volume: 24 start-page: 465 year: 2012 end-page: 477 ident: bib0102 article-title: Feature selection based on class-dependent densities for high-dimensional binary data publication-title: IEEE Trans. Knowl. Data Eng. – volume: 14 start-page: 143 year: 2004 end-page: 155 ident: bib0104 article-title: Genetic algorithms combined with discriminant analysis for key variable identification publication-title: J. Process Control – volume: 57 start-page: 320 year: 2001 ident: bib0041 article-title: Model selection and inference: a practical information – theoretic approach publication-title: Biometrics – volume: 163 start-page: 103 year: 2004 end-page: 122 ident: bib0103 article-title: Genetic wrappers for feature selection in decision tree induction and variable ordering in Bayesian network structure learning publication-title: Inf. Sci. – volume: 26 start-page: 1131 year: 2014 end-page: 1143 ident: bib0140 article-title: Efficient semi-supervised feature selection: constraint, relevance, and redundancy publication-title: IEEE Trans. Knowl. Data Eng. – volume: 111 start-page: 21 year: 2016 end-page: 31 ident: bib0147 article-title: Feature selection methods for big data bioinformatics: a survey from the search perspective publication-title: Methods – volume: 26 start-page: 392 year: 2010 end-page: 398 ident: bib0160 article-title: Robust biomarker identification for cancer diagnosis with ensemble feature selection methods publication-title: Bioinformatics – start-page: 264 year: 2014 end-page: 271 ident: bib0095 article-title: A supervised feature selection algorithm through minimum spanning tree clustering publication-title: Proceedings of International Conference on Tools with Artificial Intelligence (ICTAI) – volume: 15 start-page: 331 year: 2005 end-page: 346 ident: bib0008 article-title: Extracting nonlinear features for multispectral images by FCMC and KPCA publication-title: Digit. Signal Process. – volume: 46 start-page: 3315 year: 2013 end-page: 3327 ident: bib0084 article-title: Mutual information-based method for selecting informative feature sets publication-title: Pattern Recognit. – volume: 32 start-page: 656 year: 2011 end-page: 665 ident: bib0137 article-title: Constraint scores for semi-supervised feature selection: a comparative study publication-title: Pattern Recognit. Lett. – start-page: 660 year: 2014 end-page: 669 ident: bib0167 article-title: Towards scalable and accurate online feature selection for big data publication-title: Proceedings of IEEE International Conference on Data Mining (ICDM) – reference: Z. Zhao, F. Morstatter, S. Sharma, S. Alelyani, A. Anand, H. Liu, Advancing Feature Selection Research, ASU Feature Selection Repository (2010) 1–28. – volume: 10 start-page: 457 year: 2013 end-page: 467 ident: bib0031 article-title: Normalized feature vectors: a novel alignment-free sequence comparison method based on the numbers of adjacent amino acids publication-title: IEEE/ACM Trans. Comput. Biol. Bioinform. – volume: 26 start-page: 487 year: 2011 end-page: 500 ident: bib0108 article-title: A two-stage gene selection scheme utilizing MRMR filter and GA wrapper publication-title: Knowl. Inf. Syst. – volume: 5 start-page: 361 year: 2004 end-page: 397 ident: bib0018 article-title: Rcv1: a new benchmark collection for text categorization research publication-title: J. Mach. Learn. Res. – volume: 21 start-page: 887 year: 2008 end-page: 891 ident: bib0105 article-title: Feature selection based-on genetic algorithm for image annotation publication-title: Knowl.-Based Syst. – start-page: 1313 year: 1999 end-page: 1318 ident: bib0081 article-title: Improved mutual information feature selector for neural networks in supervised learning publication-title: Proceedings of International Joint Conference on Neural Networks (IJCNN) – volume: 5 start-page: 1531 year: 2004 end-page: 1555 ident: bib0043 article-title: Fast binary feature selection with conditional mutual information publication-title: J. Mach. Learn. Res. – volume: 20 start-page: 491 year: 2015 end-page: 499 ident: bib0100 article-title: A feature selection method for prediction essential protein publication-title: Tsinghua Sci. Technol. – volume: 35 start-page: 25 year: 1999 end-page: 39 ident: bib0119 article-title: Concept learning and feature selection based on square-error clustering publication-title: Mach. Learn. – volume: 13 start-page: 51 year: 2002 end-page: 60 ident: bib0024 article-title: A comparative study on feature selection and classification methods using gene expression profiles and proteomic patterns publication-title: Genome Inform. – volume: 2 start-page: 83 year: 2005 end-page: 101 ident: bib0094 article-title: Attribute clustering for grouping, selection, and classification of gene expression data publication-title: IEEE/ACM Trans. Comput. Biol. Bioinf. – volume: 41 start-page: 1440 year: 2008 end-page: 1451 ident: bib0139 article-title: Constraint Score: A new filter method for feature selection with pairwise constraints publication-title: Pattern Recognit. – year: 1999 ident: bib0110 article-title: Handling large unsupervised data via dimensionality reduction publication-title: Proceedings of SIGMOD Research Issues in Data Mining and Knowledge Discovery Workshop – volume: 13 start-page: 599 year: 2009 end-page: 607 ident: bib0030 article-title: Tumor clustering using nonnegative matrix factorization with gene selection publication-title: IEEE Trans. Inf. Technol. Biomed. – start-page: 1 year: 2016 end-page: 27 ident: bib0142 article-title: Semi-supervised minimum redundancy maximum relevance feature selection for audio classification publication-title: Multimedia Tools and Applications – volume: 47 start-page: 3890 year: 2014 end-page: 3906 ident: bib0051 article-title: Incremental feature selection based on rough set in dynamic incomplete data publication-title: Pattern Recognit. – start-page: 92 year: 1997 end-page: 97 ident: bib0122 article-title: Efficient feature selection in conceptual clustering publication-title: Proceedings of International Conference on Machine Learning (ICML) – start-page: 823 year: 2007 end-page: 830 ident: bib0077 article-title: Supervised feature selection via dependence estimation publication-title: Proceedings of the 24th International Conference on Machine Learning – start-page: 527 year: 2010 end-page: 532 ident: bib0132 article-title: Semi_Fisher score: a semi-supervised method for feature selection publication-title: Proceedings of International Conference on Machine Learning and Cybernetics (ICMLC) – year: 2016 ident: bib0168 article-title: Deep belief networks with feature selection for sentiment classification publication-title: Proceedings of the 7th International Conference on Intelligent Systems, Modelling and Simulation – volume: 12 start-page: 2321 year: 2015 end-page: 2325 ident: bib0172 article-title: Deep learning based feature selection for remote sensing scene classification publication-title: IEEE Geosci. Remote Sens. Lett. – volume: 24 start-page: 175 year: 2014 end-page: 186 ident: bib0068 article-title: A review of feature selection methods based on mutual information publication-title: Neural Comput. Appl. – volume: 40 start-page: 6241 year: 2013 end-page: 6252 ident: bib0109 article-title: Feature subset selection filter–wrapper based on low quality data publication-title: Expert Syst. Appl. – volume: 121 start-page: 5 year: 2013 end-page: 13 ident: bib0130 article-title: A graph Laplacian based approach to semi-supervised feature selection for regression problems publication-title: Neurocomputing – start-page: 1542 year: 2016 end-page: 1548 ident: bib0170 article-title: Layerwise feature selection in stacked sparse auto-encoder for tumor type prediction publication-title: Proceedings of IEEE International Conference on Bioinformatics and Biomedicine (BIBM) – volume: 18 start-page: 218 year: 1996 end-page: 223 ident: bib0086 article-title: Divergence based feature selection for multimodal class densities publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – year: 1996 ident: bib0063 article-title: Systematic Theory of Neural Networks for Pattern Recognition – volume: 186 start-page: 73 year: 2012 end-page: 92 ident: bib0052 article-title: Enhancing evolutionary instance selection algorithms by means of fuzzy rough set based feature selection publication-title: Inf. Sci. – volume: 32 start-page: 29 year: 2008 end-page: 38 ident: bib0106 article-title: Improved binary PSO for feature selection using gene expression data publication-title: Comput. Biol. Chem. – volume: 33 start-page: 1980 year: 2012 end-page: 1990 ident: bib0037 article-title: Feature selection on node statistics based embedding of graphs publication-title: Pattern Recognit. Lett. – year: 1995 ident: bib0015 article-title: Efficient content-based image retrieval using automatic feature selection publication-title: Proceedings of International Symposium on Computer Vision – volume: 24 start-page: 833 year: 2003 end-page: 849 ident: bib0049 article-title: Rough set methods in feature selection and recognition publication-title: Pattern Recognit. Lett. – start-page: 204 year: 2011 end-page: 218 ident: bib0138 article-title: Constrained laplacian score for semi-supervised feature selection publication-title: Proceedings of Machine Learning and Knowledge Discovery in Databases – volume: 41 start-page: 3813 year: 2008 end-page: 3821 ident: bib0048 article-title: Locally linear discriminant embedding: an efficient method for face recognition publication-title: Pattern Recognit. – start-page: 1123 year: 2010 end-page: 1126 ident: bib0131 article-title: Graph-based semi-supervised weighted band selection for classification of hyperspectral data publication-title: Proceedings of International Conference on Audio Language and Image Processing (ICALIP) – year: 2012 ident: bib0002 article-title: Feature Selection for Knowledge Discovery and Data Mining – start-page: 1022 year: 1993 end-page: 1027 ident: bib0072 article-title: Multi-interval discretization of continuous-valued attributes for classification learning publication-title: Proceedings of the 13th International Joint Conference on Artificial Intelligence – volume: 67 start-page: 301 year: 2005 end-page: 320 ident: bib0058 article-title: Regularization and variable selection via the elastic net publication-title: J. R. Stat. Soc.: Ser. B – volume: 3612 start-page: 832 year: 2005 end-page: 840 ident: bib0032 article-title: Fault feature selection based on modified binary PSO with mutation and its application in chemical process fault diagnosis publication-title: Adv. Nat. Comput. – start-page: 507 year: 2005 end-page: 514 ident: bib0116 article-title: Laplacian score for feature selection publication-title: Proceedings of Advances in Neural Information Processing Systems – start-page: 193 year: 2008 end-page: 209 ident: bib0124 article-title: Weighting method for feature selection in k-means publication-title: Computational Methods of Feature Selection – start-page: 329 year: 2011 end-page: 332 ident: bib0134 article-title: A semi-supervised method for feature selection publication-title: Proceedings of International Conference on Computational and Information Sciences (ICCIS) – volume: 34 start-page: 1184 year: 2011 end-page: 1199 ident: bib0022 article-title: Mutual information-based feature selection for intrusion detection systems publication-title: J. Netw. Comput. Appl. – volume: 43 start-page: 2763 year: 2010 end-page: 2772 ident: bib0157 article-title: Ensemble gene selection for cancer classification publication-title: Pattern Recognit. – start-page: 1 year: 2015 end-page: 7 ident: bib0114 article-title: An unsupervised attribute clustering algorithm for unsupervised feature selection publication-title: Proceedings of IEEE International Conference on Data Science and Advanced Analytics (DSAA) – volume: 28 start-page: 15 year: 2012 end-page: 23 ident: bib0014 article-title: Investigation of efficient features for image recognition by neural networks, publication-title: Neural Netw. – volume: 5 start-page: 1205 year: 2004 end-page: 1224 ident: bib0079 article-title: Efficient feature selection via analysis of relevance and redundancy publication-title: J. Mach. Learn. Res. – volume: 25 start-page: 1 year: 2013 end-page: 14 ident: bib0026 article-title: A fast clustering-based feature subset selection algorithm for high-dimensional data publication-title: IEEE Trans. Knowl. Data Eng. – start-page: 296 year: 2012 end-page: 301 ident: bib0023 article-title: Using feature selection for intrusion detection system publication-title: Proceedings of International Symposium on Communications and Information Technologies (ISCIT) – volume: 31 start-page: 226 year: 2010 end-page: 233 ident: bib0050 article-title: A rough set approach to feature selection based on ant colony optimization publication-title: Pattern Recognit. Lett. – volume: 5 start-page: 537 year: 1994 end-page: 550 ident: bib0080 article-title: Using mutual information for selecting features in supervised neural net learning publication-title: IEEE Trans. Neural Netw. – year: 1995 ident: bib0126 article-title: Neural Networks for Pattern Recognition – volume: 23 start-page: 1089 year: 1990 end-page: 1101 ident: bib0010 article-title: Rotation invariant image recognition using features selected via a systematic method publication-title: Pattern Recognit. – volume: 2 start-page: 229 year: 2010 end-page: 247 ident: bib0117 article-title: Evolutionary methods for unsupervised feature selection using Sammon's stress function publication-title: Fuzzy Inf. Eng. – volume: 39 start-page: 85 year: 2013 end-page: 94 ident: bib0017 article-title: A simultaneous feature adaptation and feature selection method for content-based image retrieval systems publication-title: Knowl.-Based Syst. – volume: 13 start-page: 971 year: 2016 end-page: 989 ident: bib0070 article-title: Supervised, unsupervised, and semi-supervised feature selection: a review on gene selection publication-title: IEEE/ACM Trans. Comput. Biol. Bioinf. – volume: 2 start-page: 108 year: 2005 end-page: 112 ident: bib0009 article-title: Using FCMC, FVS, and PCA techniques for feature extraction of multispectral images publication-title: IEEE Geosci. Remote Sens. Lett. – start-page: 133 year: 2014 end-page: 144 ident: bib0038 article-title: Gaussian based particle swarm optimisation and statistical clustering for feature selection publication-title: Proceedings of European Conference on Evolutionary Computation in Combinatorial Optimization – year: 2014 ident: bib0054 article-title: C4. 5: Programs for Machine Learning – volume: 101 start-page: 1418 year: 2006 end-page: 1429 ident: bib0059 article-title: The adaptive lasso and its oracle properties publication-title: J. Am. Stat. Assoc. – start-page: 577 year: 2008 end-page: 587 ident: bib0090 article-title: Exploration and reduction of the feature space by hierarchical clustering publication-title: Proceedings of SIAM International Conference on Data Mining – start-page: 134 year: 2015 end-page: 135 ident: bib0125 article-title: Unsupervised gene selection using particle swarm optimization and k-means publication-title: Proceedings of the Second ACM IKDD Conference on Data Sciences – volume: 5 start-page: 329 year: 2004 end-page: 340 ident: bib0146 article-title: Efficient quadratic regularization for expression arrays publication-title: Biostatistics – start-page: 592 year: 2003 end-page: 599 ident: bib0163 article-title: Online feature selection using grafting publication-title: Proceedings of the 20th International Conference on Machine Learning (ICML) – start-page: 151 year: 2012 end-page: 157 ident: bib0036 article-title: First order statistics based feature selection: a diverse and powerful family of feature seleciton techniques publication-title: Proceedings of 11th International Conference on Machine Learning and Applications (ICMLA) – volume: 22 start-page: 1855 year: 2006 end-page: 1862 ident: bib0029 article-title: Independent component analysis-based penalized discriminant method for tumor classification using gene expression data publication-title: Bioinformatics – start-page: 275 year: 2013 end-page: 283 ident: bib0047 article-title: Manifold regularized multi-task feature selection for multi-modality classification in Alzheimer's disease publication-title: Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention – volume: 20 start-page: 1425 year: 2011 end-page: 1434 ident: bib0013 article-title: Boosting color feature selection for color face recognition publication-title: IEEE Trans. Image Process. – start-page: 37 year: 2014 end-page: 64 ident: bib0066 article-title: Feature selection for classification: a review publication-title: Data Classif.: Algorithms Appl. – start-page: 944 year: 2002 end-page: 946 ident: bib0019 article-title: Improved feature selection approach TFIDF in text mining publication-title: Proceedings of International Conference on Machine Learning and Cybernetics – volume: 17 start-page: 491 year: 2005 end-page: 502 ident: bib0078 article-title: Toward integrating feature selection algorithms for classification and clustering publication-title: IEEE Trans. Knowl. Data Eng. – volume: 17 start-page: 70 year: 2013 end-page: 79 ident: bib0093 article-title: Feature selection with attributes clustering by maximal information coefficient publication-title: Procedia Comput. Sci. – volume: 86 start-page: 33 year: 2015 end-page: 45 ident: bib0069 article-title: Recent advances and emerging challenges of feature selection in the context of big data publication-title: Knowl.-Based Syst. – start-page: 293 year: 2010 end-page: 296 ident: bib0136 article-title: Semi-supervised feature selection based on label propagation and subset selection publication-title: Proceedings of International Conference on Computer and Information Application (ICCIA) – volume: 19 start-page: 2099 year: 2008 end-page: 2115 ident: bib0064 article-title: A constructive hybrid structure optimization methodology for radial basis probabilistic neural networks publication-title: IEEE Trans. Neural Netw. – volume: 43 start-page: 2068 year: 2010 end-page: 2081 ident: bib0096 article-title: Supervised feature selection by clustering using conditional mutual information-based distances publication-title: Pattern Recognit. – volume: 9 start-page: 61 year: 2013 end-page: 69 ident: bib0028 article-title: Prediction of active sites of enzymes by maximum relevance minimum redundancy (mRMR) feature selection publication-title: Mol. Biosyst. – volume: 21 start-page: 1033 year: 2010 end-page: 1047 ident: bib0046 article-title: Discriminative semi-supervised feature selection via manifold regularization publication-title: IEEE Trans. Neural Netw. – year: 2011 ident: bib0062 article-title: Data Mining: Concepts and Techniques – volume: 45 start-page: 5 year: 2001 end-page: 32 ident: bib0155 article-title: Random forests publication-title: Mach. Learn. – volume: 31 start-page: 228 year: 2009 end-page: 244 ident: bib0035 article-title: Natural image statistics and low-complexity feature selection publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – start-page: 103 year: 2008 end-page: 107 ident: bib0085 article-title: Conditional mutual information based feature selection publication-title: Proceedings of International Symposium on Knowledge Acquisition and Modeling – year: 1998 ident: bib0118 article-title: Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications – start-page: 1159 year: 2010 end-page: 1166 ident: bib0165 article-title: Online streaming feature selection publication-title: Proceedings of the 27th International Conference on Machine Learning (ICML) – volume: 62 start-page: 637 year: 2015 end-page: 646 ident: bib0033 article-title: Heterogeneous feature models and feature selection applied to bearing fault diagnosis publication-title: IEEE Trans. Ind. Electron. – start-page: 259 year: 2011 end-page: 264 ident: bib0127 article-title: Graph-based semi-supervised feature selection with application to automatic spam image identification publication-title: Computer Science for Environmental Engineering and EcoInformatics – volume: 6 start-page: 156 year: 1984 end-page: 170 ident: bib0007 article-title: A representation for shape based on peaks and ridges in the difference of low pass transform publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – start-page: 205 year: 2015 end-page: 217 ident: bib0169 article-title: Deep feature selection: theory and application to identify enhancers and promoters publication-title: Proceedings of International Conference on Research in Computational Molecular Biology – start-page: 136 year: 2005 end-page: 141 ident: bib0021 article-title: Decision tree classifier for network intrusion detection with GA-based feature selection publication-title: Proceedings of the 43rd ACM Southeast Conference – year: 2004 ident: bib0012 article-title: Scalable discriminant feature selection for image retrieval and recognition publication-title: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) – volume: 45 start-page: 531 year: 2012 end-page: 539 ident: bib0162 article-title: An ensemble of filters and classifiers for microarray data classification publication-title: Pattern Recognit. – start-page: 10 year: 2000 end-page: 15 ident: bib0148 article-title: Learning from imbalanced data sets: a comparison of various strategies publication-title: Proceedings of AAAI Workshop on Learning from Imbalanced Data Sets – start-page: 313 year: 2008 end-page: 325 ident: bib0151 article-title: Robust feature selection using ensemble feature selection techniques publication-title: Proceedings of Machine Learning and Knowledge Discovery in Databases – volume: 32 start-page: 407 year: 2004 end-page: 499 ident: bib0055 article-title: Least angle regression publication-title: Ann. Stat. – volume: 105 start-page: 713 year: 2010 end-page: 726 ident: bib0091 article-title: A framework for feature selection in clustering publication-title: J. Am. Stat. Assoc. – volume: 3 start-page: 1157 year: 2003 end-page: 1182 ident: bib0003 article-title: An introduction to variable and feature selection publication-title: J. Mach. Learn. Res. – volume: 13 start-page: 178 year: 2012 ident: bib0158 article-title: Finding minimum gene subsets with heuristic breadth-first search algorithm for robust tumor classification publication-title: BMC Bioinform. – volume: 13 start-page: 1083 year: 1999 end-page: 1101 ident: bib0060 article-title: Radial basis probabilistic neural networks: Model and application publication-title: Int. J. Pattern Recognit. Artif. Intell. – year: 2010 ident: bib0045 article-title: Feature Selection Based on Information Theory – year: 1996 ident: bib0006 article-title: Elements of Machine Learning – volume: 39 start-page: 6078 year: 2012 end-page: 6088 ident: bib0083 article-title: Feature subset selection with cumulate conditional mutual information minimization publication-title: Expert Syst. Appl. – start-page: 800 year: 2000 end-page: 803 ident: bib0111 article-title: Unsupervised color texture feature extraction and selection for soccer image segmentation publication-title: Proceedings of International Conference on Image Processing – start-page: 762 year: 2003 end-page: 769 ident: bib0011 article-title: Feature selection by maximum marginal diversity: optimality and implications for visual recognition publication-title: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition – volume: 26 start-page: 554 year: 2010 end-page: 560 ident: bib0020 article-title: Discriminative and informative features for biomolecular text mining with ensemble feature selection publication-title: Bioinformatics – volume: 42 start-page: 155 year: 2006 end-page: 165 ident: bib0087 article-title: Information gain and divergence-based feature selection for machine learning-based text categorization publication-title: Inf. Process. Manag. – volume: 27 start-page: 1226 year: 2005 end-page: 1238 ident: bib0042 article-title: Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 4 start-page: 255 year: 2004 end-page: 269 ident: bib0025 article-title: Using amino acid patterns to accurately predict translation initiation sites publication-title: In Silico Biol. – volume: 250 start-page: 45 year: 2017 end-page: 56 ident: bib0076 article-title: A supervised filter method for multi-objective feature selection in EEG classification based on multi-resolution analysis for BCI publication-title: Neurocomputing – volume: 97 start-page: 245 year: 1997 end-page: 271 ident: bib0001 article-title: Selection of relevant features and examples in machine learning publication-title: Artif. Intell. – volume: 58 start-page: 267 year: 1996 end-page: 288 ident: bib0057 article-title: Regression shrinkage and selection via the lasso publication-title: J. R. Stat. Soc.: Ser. B – volume: 38 start-page: 7746 year: 2011 end-page: 7754 ident: bib0161 article-title: Reducing dimensionality in a database of sleep EEG arousals publication-title: Expert Syst. Appl. – volume: 2 start-page: 14 year: 2011 end-page: 22 ident: bib0112 article-title: Unsupervised feature selection based on the distribution of features attributed to imbalanced data sets publication-title: Int. J. Artif. Intell. Expert Syst. – start-page: 360 year: 2016 end-page: 366 ident: bib0171 article-title: Speeding up feature selection: a deep-inspired network pruning algorithm publication-title: Proceedings of International Joint Conference on Neural Networks (IJCNN) – volume: 241 start-page: 181 year: 2017 end-page: 190 ident: bib0056 article-title: Variational relevant sample-feature machine: a fully Bayesian approach for embedded feature selection publication-title: Neurocomputing – start-page: 359 year: 2000 end-page: 366 ident: bib0073 article-title: Correlation-based feature selection of discrete and numeric class machine learning publication-title: Proceedings of 17th International Conference on Machine Learning – start-page: 979 year: 2011 end-page: 984 ident: bib0092 article-title: Feature selection using hierarchical feature clustering publication-title: Proceedings of the 20th ACM International Conference on Information and Knowledge Management – start-page: 196 year: 2009 end-page: 200 ident: bib0115 article-title: Univariate filter technique for unsupervised feature selection using a new laplacian score based local nearest neighbors publication-title: Proceedings of Asia-Pacific Conference on Information Processing – volume: 5 start-page: 845 year: 2004 end-page: 889 ident: bib0120 article-title: Feature selection for unsupervised learning publication-title: J. Mach. Learn. Res. – volume: 14 start-page: 13 year: 2013 end-page: 26 ident: bib0149 article-title: Class-imbalanced classifiers for high-dimensional data publication-title: Brief. Bioinform. – volume: 46 start-page: 389 year: 2002 end-page: 422 ident: bib0098 article-title: Gene selection for cancer classification using support vector machines publication-title: Mach. Learn. – volume: 344 start-page: 1492 year: 2014 end-page: 1496 ident: bib0061 article-title: Clustering by fast search and find of density peaks publication-title: Science – volume: 3 start-page: 1265 year: 2003 end-page: 1287 ident: bib0089 article-title: A divisive information-theoretic feature clustering algorithm for text classification publication-title: J. Mach. Learn. Res. – volume: 18 start-page: 831 year: 1996 end-page: 836 ident: bib0016 article-title: Using discriminant eigenfeatures for image retrieval publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 16 start-page: 503 year: 2006 end-page: 511 ident: bib0101 article-title: Correlation-based feature selection strategy in classification problems publication-title: Int. J. Appl. Math. Comput. Sci. – start-page: 785 year: 2011 end-page: 792 ident: bib0143 article-title: Hierarchical semantic indexing for large scale image retrieval publication-title: Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) – volume: 64 start-page: 141 year: 2017 end-page: 158 ident: bib0071 article-title: A survey on semi-supervised feature selection methods publication-title: Pattern Recognit. – year: 1995 ident: 10.1016/j.neucom.2017.11.077_bib0015 article-title: Efficient content-based image retrieval using automatic feature selection – volume: 20 start-page: 491 year: 2015 ident: 10.1016/j.neucom.2017.11.077_bib0100 article-title: A feature selection method for prediction essential protein publication-title: Tsinghua Sci. Technol. doi: 10.1109/TST.2015.7297748 – start-page: 979 year: 2011 ident: 10.1016/j.neucom.2017.11.077_bib0092 article-title: Feature selection using hierarchical feature clustering – start-page: 724 year: 1991 ident: 10.1016/j.neucom.2017.11.077_bib0121 article-title: Concept formation and attention – volume: 33 start-page: 1980 year: 2012 ident: 10.1016/j.neucom.2017.11.077_bib0037 article-title: Feature selection on node statistics based embedding of graphs publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2012.03.017 – start-page: 592 year: 2003 ident: 10.1016/j.neucom.2017.11.077_bib0163 article-title: Online feature selection using grafting – ident: 10.1016/j.neucom.2017.11.077_bib0004 – volume: 13 start-page: 51 year: 2002 ident: 10.1016/j.neucom.2017.11.077_bib0024 article-title: A comparative study on feature selection and classification methods using gene expression profiles and proteomic patterns publication-title: Genome Inform. – start-page: 1159 year: 2010 ident: 10.1016/j.neucom.2017.11.077_bib0165 article-title: Online streaming feature selection – volume: 18 start-page: 218 year: 1996 ident: 10.1016/j.neucom.2017.11.077_bib0086 article-title: Divergence based feature selection for multimodal class densities publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/34.481557 – start-page: 103 year: 2008 ident: 10.1016/j.neucom.2017.11.077_bib0085 article-title: Conditional mutual information based feature selection – volume: 18 start-page: 831 year: 1996 ident: 10.1016/j.neucom.2017.11.077_bib0016 article-title: Using discriminant eigenfeatures for image retrieval publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/34.531802 – volume: 5 start-page: 1531 year: 2004 ident: 10.1016/j.neucom.2017.11.077_bib0043 article-title: Fast binary feature selection with conditional mutual information publication-title: J. Mach. Learn. Res. – volume: 5 start-page: 1205 year: 2004 ident: 10.1016/j.neucom.2017.11.077_bib0079 article-title: Efficient feature selection via analysis of relevance and redundancy publication-title: J. Mach. Learn. Res. – start-page: 136 year: 2005 ident: 10.1016/j.neucom.2017.11.077_bib0021 article-title: Decision tree classifier for network intrusion detection with GA-based feature selection – volume: 2 start-page: 14 year: 2011 ident: 10.1016/j.neucom.2017.11.077_bib0112 article-title: Unsupervised feature selection based on the distribution of features attributed to imbalanced data sets publication-title: Int. J. Artif. Intell. Expert Syst. – start-page: 1313 year: 1999 ident: 10.1016/j.neucom.2017.11.077_bib0081 article-title: Improved mutual information feature selector for neural networks in supervised learning – start-page: 1123 year: 2010 ident: 10.1016/j.neucom.2017.11.077_bib0131 article-title: Graph-based semi-supervised weighted band selection for classification of hyperspectral data – volume: 45 start-page: 5 year: 2001 ident: 10.1016/j.neucom.2017.11.077_bib0155 article-title: Random forests publication-title: Mach. Learn. doi: 10.1023/A:1010933404324 – volume: 250 start-page: 45 year: 2017 ident: 10.1016/j.neucom.2017.11.077_bib0076 article-title: A supervised filter method for multi-objective feature selection in EEG classification based on multi-resolution analysis for BCI publication-title: Neurocomputing doi: 10.1016/j.neucom.2016.09.123 – year: 1996 ident: 10.1016/j.neucom.2017.11.077_bib0063 – volume: 9 start-page: 61 year: 2013 ident: 10.1016/j.neucom.2017.11.077_bib0028 article-title: Prediction of active sites of enzymes by maximum relevance minimum redundancy (mRMR) feature selection publication-title: Mol. Biosyst. doi: 10.1039/C2MB25327E – year: 1995 ident: 10.1016/j.neucom.2017.11.077_bib0126 – start-page: 41 year: 2008 ident: 10.1016/j.neucom.2017.11.077_bib0027 article-title: A novel unsupervised feature selection method for bioinformatics data sets through feature clustering – volume: 3612 start-page: 832 year: 2005 ident: 10.1016/j.neucom.2017.11.077_bib0032 article-title: Fault feature selection based on modified binary PSO with mutation and its application in chemical process fault diagnosis publication-title: Adv. Nat. Comput. – volume: 43 start-page: 2068 year: 2010 ident: 10.1016/j.neucom.2017.11.077_bib0096 article-title: Supervised feature selection by clustering using conditional mutual information-based distances publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2009.12.013 – volume: 26 start-page: 554 year: 2010 ident: 10.1016/j.neucom.2017.11.077_bib0020 article-title: Discriminative and informative features for biomolecular text mining with ensemble feature selection publication-title: Bioinformatics doi: 10.1093/bioinformatics/btq381 – volume: 10 start-page: 457 year: 2013 ident: 10.1016/j.neucom.2017.11.077_bib0031 article-title: Normalized feature vectors: a novel alignment-free sequence comparison method based on the numbers of adjacent amino acids publication-title: IEEE/ACM Trans. Comput. Biol. Bioinform. doi: 10.1109/TCBB.2013.10 – start-page: 1 year: 2006 ident: 10.1016/j.neucom.2017.11.077_bib0040 article-title: Information theory for Gabor feature selection for face recognition publication-title: EURASIP J. Appl. Signal Process. – volume: 24 start-page: 175 year: 2014 ident: 10.1016/j.neucom.2017.11.077_bib0068 article-title: A review of feature selection methods based on mutual information publication-title: Neural Comput. Appl. doi: 10.1007/s00521-013-1368-0 – start-page: 664 year: 2013 ident: 10.1016/j.neucom.2017.11.077_bib0133 article-title: Manifold based fisher method for semi-supervised feature selection – volume: 21 start-page: 1033 year: 2010 ident: 10.1016/j.neucom.2017.11.077_bib0046 article-title: Discriminative semi-supervised feature selection via manifold regularization publication-title: IEEE Trans. Neural Netw. doi: 10.1109/TNN.2010.2047114 – volume: 163 start-page: 103 year: 2004 ident: 10.1016/j.neucom.2017.11.077_bib0103 article-title: Genetic wrappers for feature selection in decision tree induction and variable ordering in Bayesian network structure learning publication-title: Inf. Sci. doi: 10.1016/j.ins.2003.03.019 – volume: 13 start-page: 1083 year: 1999 ident: 10.1016/j.neucom.2017.11.077_bib0060 article-title: Radial basis probabilistic neural networks: Model and application publication-title: Int. J. Pattern Recognit. Artif. Intell. doi: 10.1142/S0218001499000604 – start-page: 1022 year: 1993 ident: 10.1016/j.neucom.2017.11.077_bib0072 article-title: Multi-interval discretization of continuous-valued attributes for classification learning – volume: 27 start-page: 1226 year: 2005 ident: 10.1016/j.neucom.2017.11.077_bib0042 article-title: Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2005.159 – start-page: 10 year: 2000 ident: 10.1016/j.neucom.2017.11.077_bib0148 article-title: Learning from imbalanced data sets: a comparison of various strategies – volume: 31 start-page: 226 year: 2010 ident: 10.1016/j.neucom.2017.11.077_bib0050 article-title: A rough set approach to feature selection based on ant colony optimization publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2009.10.013 – volume: 86 start-page: 33 year: 2015 ident: 10.1016/j.neucom.2017.11.077_bib0069 article-title: Recent advances and emerging challenges of feature selection in the context of big data publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2015.05.014 – volume: 4 start-page: 255 year: 2004 ident: 10.1016/j.neucom.2017.11.077_bib0025 article-title: Using amino acid patterns to accurately predict translation initiation sites publication-title: In Silico Biol. doi: 10.3233/ISB-00132 – volume: 24 start-page: 833 year: 2003 ident: 10.1016/j.neucom.2017.11.077_bib0049 article-title: Rough set methods in feature selection and recognition publication-title: Pattern Recognit. Lett. doi: 10.1016/S0167-8655(02)00196-4 – volume: 111 start-page: 21 year: 2016 ident: 10.1016/j.neucom.2017.11.077_bib0147 article-title: Feature selection methods for big data bioinformatics: a survey from the search perspective publication-title: Methods doi: 10.1016/j.ymeth.2016.08.014 – start-page: 196 year: 2009 ident: 10.1016/j.neucom.2017.11.077_bib0115 article-title: Univariate filter technique for unsupervised feature selection using a new laplacian score based local nearest neighbors – volume: 24 start-page: 301 year: 2002 ident: 10.1016/j.neucom.2017.11.077_bib0113 article-title: Unsupervised feature selection using feature similarity publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/34.990133 – volume: 121 start-page: 5 year: 2013 ident: 10.1016/j.neucom.2017.11.077_bib0130 article-title: A graph Laplacian based approach to semi-supervised feature selection for regression problems publication-title: Neurocomputing doi: 10.1016/j.neucom.2012.10.028 – volume: 74 start-page: 2941 year: 2011 ident: 10.1016/j.neucom.2017.11.077_bib0034 article-title: Feature selection for high-dimensional machinery fault diagnosis data using multiple models and Radial Basis Function networks publication-title: Neurocomputing doi: 10.1016/j.neucom.2011.03.043 – volume: 64 start-page: 141 year: 2017 ident: 10.1016/j.neucom.2017.11.077_bib0071 article-title: A survey on semi-supervised feature selection methods publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2016.11.003 – start-page: 1113 year: 2009 ident: 10.1016/j.neucom.2017.11.077_bib0145 article-title: Feature hashing for large scale multitask learning – year: 1998 ident: 10.1016/j.neucom.2017.11.077_bib0118 – volume: 26 start-page: 1131 year: 2014 ident: 10.1016/j.neucom.2017.11.077_bib0140 article-title: Efficient semi-supervised feature selection: constraint, relevance, and redundancy publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2013.86 – volume: 38 start-page: 7746 year: 2011 ident: 10.1016/j.neucom.2017.11.077_bib0161 article-title: Reducing dimensionality in a database of sleep EEG arousals publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2010.12.134 – volume: 13 start-page: 178 year: 2012 ident: 10.1016/j.neucom.2017.11.077_bib0158 article-title: Finding minimum gene subsets with heuristic breadth-first search algorithm for robust tumor classification publication-title: BMC Bioinform. doi: 10.1186/1471-2105-13-178 – year: 2016 ident: 10.1016/j.neucom.2017.11.077_bib0168 article-title: Deep belief networks with feature selection for sentiment classification – volume: 18 start-page: 261 year: 2014 ident: 10.1016/j.neucom.2017.11.077_bib0107 article-title: Particle swarm optimisation for feature selection in classification: novel initialisation and updating mechanisms publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2013.09.018 – volume: 186 start-page: 73 year: 2012 ident: 10.1016/j.neucom.2017.11.077_bib0052 article-title: Enhancing evolutionary instance selection algorithms by means of fuzzy rough set based feature selection publication-title: Inf. Sci. doi: 10.1016/j.ins.2011.09.027 – volume: 71 start-page: 1842 year: 2008 ident: 10.1016/j.neucom.2017.11.077_bib0128 article-title: Locality sensitive semi-supervised feature selection publication-title: Neurocomputing doi: 10.1016/j.neucom.2007.06.014 – volume: 43 start-page: 2763 year: 2010 ident: 10.1016/j.neucom.2017.11.077_bib0157 article-title: Ensemble gene selection for cancer classification publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2010.02.008 – volume: 57 start-page: 320 year: 2001 ident: 10.1016/j.neucom.2017.11.077_bib0041 article-title: Model selection and inference: a practical information – theoretic approach publication-title: Biometrics – start-page: 433 year: 1999 ident: 10.1016/j.neucom.2017.11.077_bib0123 article-title: Model selection in unsupervised learning with applications to document clustering – volume: 2 start-page: 83 year: 2005 ident: 10.1016/j.neucom.2017.11.077_bib0094 article-title: Attribute clustering for grouping, selection, and classification of gene expression data publication-title: IEEE/ACM Trans. Comput. Biol. Bioinf. doi: 10.1109/TCBB.2005.17 – volume: 3 start-page: 1157 year: 2003 ident: 10.1016/j.neucom.2017.11.077_bib0003 article-title: An introduction to variable and feature selection publication-title: J. Mach. Learn. Res. – volume: 241 start-page: 181 year: 2017 ident: 10.1016/j.neucom.2017.11.077_bib0056 article-title: Variational relevant sample-feature machine: a fully Bayesian approach for embedded feature selection publication-title: Neurocomputing doi: 10.1016/j.neucom.2017.02.057 – start-page: 205 year: 2015 ident: 10.1016/j.neucom.2017.11.077_bib0169 article-title: Deep feature selection: theory and application to identify enhancers and promoters – volume: 47 start-page: 3429 year: 2014 ident: 10.1016/j.neucom.2017.11.077_bib0159 article-title: Random forests with ensemble of feature spaces publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2014.04.001 – start-page: 507 year: 2005 ident: 10.1016/j.neucom.2017.11.077_bib0116 article-title: Laplacian score for feature selection – volume: 105 start-page: 713 year: 2010 ident: 10.1016/j.neucom.2017.11.077_bib0091 article-title: A framework for feature selection in clustering publication-title: J. Am. Stat. Assoc. doi: 10.1198/jasa.2010.tm09415 – volume: 28 start-page: 15 year: 2012 ident: 10.1016/j.neucom.2017.11.077_bib0014 article-title: Investigation of efficient features for image recognition by neural networks, publication-title: Neural Netw. doi: 10.1016/j.neunet.2011.12.002 – volume: 17 start-page: 491 year: 2005 ident: 10.1016/j.neucom.2017.11.077_bib0078 article-title: Toward integrating feature selection algorithms for classification and clustering publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2005.66 – start-page: 193 year: 2008 ident: 10.1016/j.neucom.2017.11.077_bib0124 article-title: Weighting method for feature selection in k-means – year: 1999 ident: 10.1016/j.neucom.2017.11.077_bib0110 article-title: Handling large unsupervised data via dimensionality reduction – volume: 19 start-page: 2099 year: 2008 ident: 10.1016/j.neucom.2017.11.077_bib0064 article-title: A constructive hybrid structure optimization methodology for radial basis probabilistic neural networks publication-title: IEEE Trans. Neural Netw. doi: 10.1109/TNN.2008.2004370 – year: 2012 ident: 10.1016/j.neucom.2017.11.077_bib0002 – volume: 105 start-page: 12 year: 2013 ident: 10.1016/j.neucom.2017.11.077_bib0135 article-title: Efficient semi-supervised feature selection with noise insensitive trace ratio criterion publication-title: Neurocomputing doi: 10.1016/j.neucom.2012.05.031 – volume: 23 start-page: 1089 year: 1990 ident: 10.1016/j.neucom.2017.11.077_bib0010 article-title: Rotation invariant image recognition using features selected via a systematic method publication-title: Pattern Recognit. doi: 10.1016/0031-3203(90)90005-6 – volume: 185 start-page: 1026 year: 2007 ident: 10.1016/j.neucom.2017.11.077_bib0065 article-title: A hybrid particle swarm optimization–back-propagation algorithm for feedforward neural network training publication-title: Appl. Math. Comput. – volume: 14 start-page: 143 year: 2004 ident: 10.1016/j.neucom.2017.11.077_bib0104 article-title: Genetic algorithms combined with discriminant analysis for key variable identification publication-title: J. Process Control doi: 10.1016/S0959-1524(03)00029-5 – volume: 7 start-page: 1861 year: 2006 ident: 10.1016/j.neucom.2017.11.077_bib0164 article-title: Streamwise feature selection publication-title: J. Mach. Learn. Res. – start-page: 762 year: 2003 ident: 10.1016/j.neucom.2017.11.077_bib0011 article-title: Feature selection by maximum marginal diversity: optimality and implications for visual recognition – start-page: 360 year: 2016 ident: 10.1016/j.neucom.2017.11.077_bib0171 article-title: Speeding up feature selection: a deep-inspired network pruning algorithm – year: 2010 ident: 10.1016/j.neucom.2017.11.077_bib0045 – year: 2004 ident: 10.1016/j.neucom.2017.11.077_bib0012 article-title: Scalable discriminant feature selection for image retrieval and recognition – volume: 47 start-page: 3890 year: 2014 ident: 10.1016/j.neucom.2017.11.077_bib0051 article-title: Incremental feature selection based on rough set in dynamic incomplete data publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2014.06.002 – volume: 5 start-page: 329 year: 2004 ident: 10.1016/j.neucom.2017.11.077_bib0146 article-title: Efficient quadratic regularization for expression arrays publication-title: Biostatistics doi: 10.1093/biostatistics/kxh010 – volume: 5 start-page: 537 year: 1994 ident: 10.1016/j.neucom.2017.11.077_bib0080 article-title: Using mutual information for selecting features in supervised neural net learning publication-title: IEEE Trans. Neural Netw. doi: 10.1109/72.298224 – start-page: 204 year: 2011 ident: 10.1016/j.neucom.2017.11.077_bib0138 article-title: Constrained laplacian score for semi-supervised feature selection – volume: 25 start-page: 1 year: 2013 ident: 10.1016/j.neucom.2017.11.077_bib0026 article-title: A fast clustering-based feature subset selection algorithm for high-dimensional data publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2011.181 – start-page: 259 year: 2011 ident: 10.1016/j.neucom.2017.11.077_bib0127 article-title: Graph-based semi-supervised feature selection with application to automatic spam image identification – volume: 1 start-page: 2425 year: 2010 ident: 10.1016/j.neucom.2017.11.077_bib0053 article-title: Rough set and Tabu search based feature selection for credit scoring publication-title: Procedia Comput. Sci. doi: 10.1016/j.procs.2010.04.273 – start-page: 134 year: 2015 ident: 10.1016/j.neucom.2017.11.077_bib0125 article-title: Unsupervised gene selection using particle swarm optimization and k-means – volume: 67 start-page: 301 year: 2005 ident: 10.1016/j.neucom.2017.11.077_bib0058 article-title: Regularization and variable selection via the elastic net publication-title: J. R. Stat. Soc.: Ser. B doi: 10.1111/j.1467-9868.2005.00503.x – volume: 46 start-page: 3315 year: 2013 ident: 10.1016/j.neucom.2017.11.077_bib0084 article-title: Mutual information-based method for selecting informative feature sets publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2013.04.021 – volume: 40 start-page: 6241 year: 2013 ident: 10.1016/j.neucom.2017.11.077_bib0109 article-title: Feature subset selection filter–wrapper based on low quality data publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2013.05.051 – start-page: 133 year: 2014 ident: 10.1016/j.neucom.2017.11.077_bib0038 article-title: Gaussian based particle swarm optimisation and statistical clustering for feature selection – start-page: 151 year: 2012 ident: 10.1016/j.neucom.2017.11.077_bib0036 article-title: First order statistics based feature selection: a diverse and powerful family of feature seleciton techniques – volume: 35 start-page: 1178 year: 2013 ident: 10.1016/j.neucom.2017.11.077_bib0166 article-title: Online feature selection with streaming features publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2012.197 – volume: 9 start-page: 1106 year: 2012 ident: 10.1016/j.neucom.2017.11.077_bib0152 article-title: A survey on filter techniques for feature selection in gene expression microarray analysis publication-title: IEEE/ACM Trans. Comput. Biol. Bioinf. doi: 10.1109/TCBB.2012.33 – start-page: 248 year: 2011 ident: 10.1016/j.neucom.2017.11.077_bib0129 article-title: Graph Laplacian for semi-supervised feature selection in regression problems – volume: 41 start-page: 1440 year: 2008 ident: 10.1016/j.neucom.2017.11.077_bib0139 article-title: Constraint Score: A new filter method for feature selection with pairwise constraints publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2007.10.009 – volume: 344 start-page: 1492 year: 2014 ident: 10.1016/j.neucom.2017.11.077_bib0061 article-title: Clustering by fast search and find of density peaks publication-title: Science doi: 10.1126/science.1242072 – start-page: 577 year: 2008 ident: 10.1016/j.neucom.2017.11.077_bib0090 article-title: Exploration and reduction of the feature space by hierarchical clustering – volume: 3 start-page: 1265 year: 2003 ident: 10.1016/j.neucom.2017.11.077_bib0089 article-title: A divisive information-theoretic feature clustering algorithm for text classification publication-title: J. Mach. Learn. Res. – volume: 16 start-page: 503 year: 2006 ident: 10.1016/j.neucom.2017.11.077_bib0101 article-title: Correlation-based feature selection strategy in classification problems publication-title: Int. J. Appl. Math. Comput. Sci. – volume: 14 start-page: 13 year: 2013 ident: 10.1016/j.neucom.2017.11.077_bib0149 article-title: Class-imbalanced classifiers for high-dimensional data publication-title: Brief. Bioinform. doi: 10.1093/bib/bbs006 – start-page: 785 year: 2011 ident: 10.1016/j.neucom.2017.11.077_bib0143 article-title: Hierarchical semantic indexing for large scale image retrieval – volume: 62 start-page: 637 year: 2015 ident: 10.1016/j.neucom.2017.11.077_bib0033 article-title: Heterogeneous feature models and feature selection applied to bearing fault diagnosis publication-title: IEEE Trans. Ind. Electron. doi: 10.1109/TIE.2014.2327589 – volume: 19 start-page: 57 year: 2014 ident: 10.1016/j.neucom.2017.11.077_bib0039 article-title: Statistics-based wrapper for feature selection: an implementation on financial distress identification with support vector machine publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2014.01.018 – year: 2014 ident: 10.1016/j.neucom.2017.11.077_bib0054 – volume: 101 start-page: 1418 year: 2006 ident: 10.1016/j.neucom.2017.11.077_bib0059 article-title: The adaptive lasso and its oracle properties publication-title: J. Am. Stat. Assoc. doi: 10.1198/016214506000000735 – volume: 6 start-page: 156 year: 1984 ident: 10.1016/j.neucom.2017.11.077_bib0007 article-title: A representation for shape based on peaks and ridges in the difference of low pass transform publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.1984.4767500 – start-page: 275 year: 2013 ident: 10.1016/j.neucom.2017.11.077_bib0047 article-title: Manifold regularized multi-task feature selection for multi-modality classification in Alzheimer's disease – volume: 13 start-page: 971 year: 2016 ident: 10.1016/j.neucom.2017.11.077_bib0070 article-title: Supervised, unsupervised, and semi-supervised feature selection: a review on gene selection publication-title: IEEE/ACM Trans. Comput. Biol. Bioinf. doi: 10.1109/TCBB.2015.2478454 – start-page: 313 year: 2008 ident: 10.1016/j.neucom.2017.11.077_bib0151 article-title: Robust feature selection using ensemble feature selection techniques – volume: 39 start-page: 6078 year: 2012 ident: 10.1016/j.neucom.2017.11.077_bib0083 article-title: Feature subset selection with cumulate conditional mutual information minimization publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2011.12.003 – volume: 21 start-page: 887 year: 2008 ident: 10.1016/j.neucom.2017.11.077_bib0105 article-title: Feature selection based-on genetic algorithm for image annotation publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2008.03.051 – volume: 45 start-page: 531 year: 2012 ident: 10.1016/j.neucom.2017.11.077_bib0162 article-title: An ensemble of filters and classifiers for microarray data classification publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2011.06.006 – volume: 41 start-page: 3813 year: 2008 ident: 10.1016/j.neucom.2017.11.077_bib0048 article-title: Locally linear discriminant embedding: an efficient method for face recognition publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2008.05.027 – year: 2011 ident: 10.1016/j.neucom.2017.11.077_bib0062 – start-page: 1 year: 2015 ident: 10.1016/j.neucom.2017.11.077_bib0114 article-title: An unsupervised attribute clustering algorithm for unsupervised feature selection – volume: 58 start-page: 267 year: 1996 ident: 10.1016/j.neucom.2017.11.077_bib0057 article-title: Regression shrinkage and selection via the lasso publication-title: J. R. Stat. Soc.: Ser. B doi: 10.1111/j.2517-6161.1996.tb02080.x – volume: 101 start-page: 32 year: 2013 ident: 10.1016/j.neucom.2017.11.077_bib0088 article-title: Divergence-based feature selection for separate classes publication-title: Neurocomputing doi: 10.1016/j.neucom.2012.06.036 – start-page: 527 year: 2010 ident: 10.1016/j.neucom.2017.11.077_bib0132 article-title: Semi_Fisher score: a semi-supervised method for feature selection – volume: 15 start-page: 331 year: 2005 ident: 10.1016/j.neucom.2017.11.077_bib0008 article-title: Extracting nonlinear features for multispectral images by FCMC and KPCA publication-title: Digit. Signal Process. doi: 10.1016/j.dsp.2004.12.004 – volume: 31 start-page: 228 year: 2009 ident: 10.1016/j.neucom.2017.11.077_bib0035 article-title: Natural image statistics and low-complexity feature selection publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2008.77 – volume: 97 start-page: 245 year: 1997 ident: 10.1016/j.neucom.2017.11.077_bib0001 article-title: Selection of relevant features and examples in machine learning publication-title: Artif. Intell. doi: 10.1016/S0004-3702(97)00063-5 – volume: 39 start-page: 85 year: 2013 ident: 10.1016/j.neucom.2017.11.077_bib0017 article-title: A simultaneous feature adaptation and feature selection method for content-based image retrieval systems publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2012.10.011 – volume: 12 start-page: 2321 year: 2015 ident: 10.1016/j.neucom.2017.11.077_bib0172 article-title: Deep learning based feature selection for remote sensing scene classification publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2015.2475299 – start-page: 944 year: 2002 ident: 10.1016/j.neucom.2017.11.077_bib0019 article-title: Improved feature selection approach TFIDF in text mining – volume: 2 start-page: 229 year: 2010 ident: 10.1016/j.neucom.2017.11.077_bib0117 article-title: Evolutionary methods for unsupervised feature selection using Sammon's stress function publication-title: Fuzzy Inf. Eng. doi: 10.1007/s12543-010-0047-4 – volume: 20 start-page: 1425 year: 2011 ident: 10.1016/j.neucom.2017.11.077_bib0013 article-title: Boosting color feature selection for color face recognition publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2010.2093906 – volume: 173 start-page: 346 year: 2016 ident: 10.1016/j.neucom.2017.11.077_bib0097 article-title: A novel features ranking metric with application to scalable visual and bioinformatics data classification publication-title: Neurocomputing doi: 10.1016/j.neucom.2014.12.123 – start-page: 296 year: 2012 ident: 10.1016/j.neucom.2017.11.077_bib0023 article-title: Using feature selection for intrusion detection system – volume: 2 start-page: 108 year: 2005 ident: 10.1016/j.neucom.2017.11.077_bib0009 article-title: Using FCMC, FVS, and PCA techniques for feature extraction of multispectral images publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2005.844169 – start-page: 800 year: 2000 ident: 10.1016/j.neucom.2017.11.077_bib0111 article-title: Unsupervised color texture feature extraction and selection for soccer image segmentation – volume: 5 start-page: 845 year: 2004 ident: 10.1016/j.neucom.2017.11.077_bib0120 article-title: Feature selection for unsupervised learning publication-title: J. Mach. Learn. Res. – volume: 34 start-page: 1184 year: 2011 ident: 10.1016/j.neucom.2017.11.077_bib0022 article-title: Mutual information-based feature selection for intrusion detection systems publication-title: J. Netw. Comput. Appl. doi: 10.1016/j.jnca.2011.01.002 – volume: 32 start-page: 407 year: 2004 ident: 10.1016/j.neucom.2017.11.077_bib0055 article-title: Least angle regression publication-title: Ann. Stat. doi: 10.1214/009053604000000067 – volume: 12 start-page: 687 year: 1999 ident: 10.1016/j.neucom.2017.11.077_bib0044 article-title: Data visualization and feature selection: new algorithms for nongaussian data publication-title: Adv. Neural Inf. Process. Syst. – start-page: 249 year: 1992 ident: 10.1016/j.neucom.2017.11.077_bib0074 article-title: A practical approach to feature selection – start-page: 171 year: 1994 ident: 10.1016/j.neucom.2017.11.077_bib0075 article-title: Estimating attributes: analysis and extensions of RELIEF – volume: 35 start-page: 25 year: 1999 ident: 10.1016/j.neucom.2017.11.077_bib0119 article-title: Concept learning and feature selection based on square-error clustering publication-title: Mach. Learn. doi: 10.1023/A:1007567018844 – volume: 29 start-page: 110 year: 2013 ident: 10.1016/j.neucom.2017.11.077_bib0067 article-title: Feature selection for clustering: a revie publication-title: Data Clust.: Algorithms Appl. – volume: 46 start-page: 389 year: 2002 ident: 10.1016/j.neucom.2017.11.077_bib0098 article-title: Gene selection for cancer classification using support vector machines publication-title: Mach. Learn. doi: 10.1023/A:1012487302797 – volume: 24 start-page: 465 year: 2012 ident: 10.1016/j.neucom.2017.11.077_bib0102 article-title: Feature selection based on class-dependent densities for high-dimensional binary data publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2010.263 – start-page: 264 year: 2014 ident: 10.1016/j.neucom.2017.11.077_bib0095 article-title: A supervised feature selection algorithm through minimum spanning tree clustering – start-page: 1542 year: 2016 ident: 10.1016/j.neucom.2017.11.077_bib0170 article-title: Layerwise feature selection in stacked sparse auto-encoder for tumor type prediction – start-page: 329 year: 2011 ident: 10.1016/j.neucom.2017.11.077_bib0134 article-title: A semi-supervised method for feature selection – start-page: 359 year: 2000 ident: 10.1016/j.neucom.2017.11.077_bib0073 article-title: Correlation-based feature selection of discrete and numeric class machine learning – volume: 20 start-page: 832 year: 1998 ident: 10.1016/j.neucom.2017.11.077_bib0153 article-title: The random subspace method for constructing decision forests publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/34.709601 – start-page: 37 year: 2014 ident: 10.1016/j.neucom.2017.11.077_bib0066 article-title: Feature selection for classification: a review publication-title: Data Classif.: Algorithms Appl. – volume: 22 start-page: 1855 year: 2006 ident: 10.1016/j.neucom.2017.11.077_bib0029 article-title: Independent component analysis-based penalized discriminant method for tumor classification using gene expression data publication-title: Bioinformatics doi: 10.1093/bioinformatics/btl190 – volume: 54 start-page: 1535 year: 2010 ident: 10.1016/j.neucom.2017.11.077_bib0156 article-title: Ensemble classification based on generalized additive models publication-title: Comput. Stat. Data Anal. doi: 10.1016/j.csda.2009.12.013 – volume: 32 start-page: 29 year: 2008 ident: 10.1016/j.neucom.2017.11.077_bib0106 article-title: Improved binary PSO for feature selection using gene expression data publication-title: Comput. Biol. Chem. doi: 10.1016/j.compbiolchem.2007.09.005 – volume: 17 start-page: 70 year: 2013 ident: 10.1016/j.neucom.2017.11.077_bib0093 article-title: Feature selection with attributes clustering by maximal information coefficient publication-title: Procedia Comput. Sci. doi: 10.1016/j.procs.2013.05.011 – start-page: 293 year: 2010 ident: 10.1016/j.neucom.2017.11.077_bib0136 article-title: Semi-supervised feature selection based on label propagation and subset selection – start-page: 660 year: 2014 ident: 10.1016/j.neucom.2017.11.077_bib0167 article-title: Towards scalable and accurate online feature selection for big data – start-page: 823 year: 2007 ident: 10.1016/j.neucom.2017.11.077_bib0077 article-title: Supervised feature selection via dependence estimation – start-page: 379 year: 1999 ident: 10.1016/j.neucom.2017.11.077_bib0150 article-title: Feature selection for ensembles – volume: 26 start-page: 392 year: 2010 ident: 10.1016/j.neucom.2017.11.077_bib0160 article-title: Robust biomarker identification for cancer diagnosis with ensemble feature selection methods publication-title: Bioinformatics doi: 10.1093/bioinformatics/btp630 – year: 1996 ident: 10.1016/j.neucom.2017.11.077_bib0006 – volume: 26 start-page: 487 year: 2011 ident: 10.1016/j.neucom.2017.11.077_bib0108 article-title: A two-stage gene selection scheme utilizing MRMR filter and GA wrapper publication-title: Knowl. Inf. Syst. doi: 10.1007/s10115-010-0288-x – start-page: 1 year: 2016 ident: 10.1016/j.neucom.2017.11.077_bib0142 article-title: Semi-supervised minimum redundancy maximum relevance feature selection for audio classification – volume: 15 start-page: 1371 year: 2014 ident: 10.1016/j.neucom.2017.11.077_bib0144 article-title: Towards ultrahigh dimensional feature selection for big data publication-title: J. Mach. Learn. Res. – start-page: 245 year: 1994 ident: 10.1016/j.neucom.2017.11.077_bib0005 article-title: Selection of relevant features in machine learning – start-page: 92 year: 1997 ident: 10.1016/j.neucom.2017.11.077_bib0122 article-title: Efficient feature selection in conceptual clustering – volume: 51 start-page: 6166 year: 2007 ident: 10.1016/j.neucom.2017.11.077_bib0154 article-title: Classification by ensembles from random partitions of high-dimensional data publication-title: Comput. Stat. Data Anal. doi: 10.1016/j.csda.2006.12.043 – volume: 5 start-page: 361 year: 2004 ident: 10.1016/j.neucom.2017.11.077_bib0018 article-title: Rcv1: a new benchmark collection for text categorization research publication-title: J. Mach. Learn. Res. – volume: 13 start-page: 599 year: 2009 ident: 10.1016/j.neucom.2017.11.077_bib0030 article-title: Tumor clustering using nonnegative matrix factorization with gene selection publication-title: IEEE Trans. Inf. Technol. Biomed. doi: 10.1109/TITB.2009.2018115 – volume: 61 start-page: 511 year: 2017 ident: 10.1016/j.neucom.2017.11.077_bib0141 article-title: An efficient semi-supervised representatives feature selection algorithm based on information theory publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2016.08.011 – volume: 2 start-page: 110 year: 2005 ident: 10.1016/j.neucom.2017.11.077_bib0099 article-title: Semisupervised learning for molecular profiling publication-title: IEEE/ACM Trans. Comput. Biol. Bioinf. doi: 10.1109/TCBB.2005.28 – volume: 32 start-page: 656 year: 2011 ident: 10.1016/j.neucom.2017.11.077_bib0137 article-title: Constraint scores for semi-supervised feature selection: a comparative study publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2010.12.014 – start-page: 417 year: 2007 ident: 10.1016/j.neucom.2017.11.077_bib0082 article-title: Conditional mutual information based feature selection for classification task – volume: 42 start-page: 155 year: 2006 ident: 10.1016/j.neucom.2017.11.077_bib0087 article-title: Information gain and divergence-based feature selection for machine learning-based text categorization publication-title: Inf. Process. Manag. doi: 10.1016/j.ipm.2004.08.006 |
| SSID | ssj0017129 |
| Score | 2.7031171 |
| Snippet | High-dimensional data analysis is a challenge for researchers and engineers in the fields of machine learning and data mining. Feature selection provides an... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 70 |
| SubjectTerms | Data mining Dimensionality reduction Feature selection Machine learning |
| Title | Feature selection in machine learning: A new perspective |
| URI | https://dx.doi.org/10.1016/j.neucom.2017.11.077 |
| Volume | 300 |
| WOSCitedRecordID | wos000432490900005&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: ScienceDirect Freedom Collection - Elsevier customDbUrl: eissn: 1872-8286 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017129 issn: 0925-2312 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1LT9wwELbapYde6FuFPuRDr0FObMcOtxWiahFCPdBqb5GxnTaopIjdBX4-43hsllLRcuglSqzEecynmcl4Zj5CPtSutiV3opDOVoWwhhWmg0MtjfWmY06MyZjf9tXBgZ7Nmi9YXTIf6QTUMOjLy-b0v4oaxkDYoXT2HuLOk8IA7IPQYQtih-0_CT44dWFVYD4y3GAq48mYM-kTScT3WJAOLnXoW7xabZkc1bFphx0pHzCYMD0JPRVcAFAOHuxEMuu9PsNjfxmXcnpz4fvraD0GpX-EvPesaPKoR_OJ0YdSh7BmLHFPYcRKFuAj3tConLEVnRiJQdC6RuaYW3o7hhCOtwa_DEk8cCe1FZqrIsXLjTbZv5mvnFSY8tWO2zhLG2aBP5wWZnlI1iolGz0ha9PPu7O9vNCkyiq2Y8TXSNWVYwrg7af5s_ey4pEcPiXr-CtBpxECz8gDPzwnTxJNB0Wt_YJoRATNiKD9QBERNCFim04p4IGu4OEl-fpx93DnU4GEGYXlqloUoipNbZ2ykh1pXje2O-pkoJMxTNTSceE7LW3JDFO1F9Y503iuZVWD9dXKcf6KTIZfg39NaOcqw5kRXDshvAcvGmxBzV2nuZfO-w3C04doLXaTD6QmP9u7xLBBinzVaeym8pfzVfrGLXqE0dNrATh3Xrl5zzu9IY-v4f2WTBZnS_-OPLLni35-9h5RcwVy-4M- |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Feature+selection+in+machine+learning%3A+A+new+perspective&rft.jtitle=Neurocomputing+%28Amsterdam%29&rft.au=Cai%2C+Jie&rft.au=Luo%2C+Jiawei&rft.au=Wang%2C+Shulin&rft.au=Yang%2C+Sheng&rft.date=2018-07-26&rft.issn=0925-2312&rft.volume=300&rft.spage=70&rft.epage=79&rft_id=info:doi/10.1016%2Fj.neucom.2017.11.077&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_neucom_2017_11_077 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0925-2312&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0925-2312&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0925-2312&client=summon |