Diversity techniques improve the performance of the best imbalance learning ensembles
Many real-life problems can be described as unbalanced, where the number of instances belonging to one of the classes is much larger than the numbers in other classes. Examples are spam detection, credit card fraud detection or medical diagnosis. Ensembles of classifiers have acquired popularity in...
Uloženo v:
| Vydáno v: | Information sciences Ročník 325; s. 98 - 117 |
|---|---|
| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Inc
20.12.2015
|
| Témata: | |
| ISSN: | 0020-0255, 1872-6291 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Many real-life problems can be described as unbalanced, where the number of instances belonging to one of the classes is much larger than the numbers in other classes. Examples are spam detection, credit card fraud detection or medical diagnosis. Ensembles of classifiers have acquired popularity in this kind of problems for their ability to obtain better results than individual classifiers. The most commonly used techniques by those ensembles especially designed to deal with imbalanced problems are for example Re-weighting, Oversampling and Undersampling. Other techniques, originally intended to increase the ensemble diversity, have not been systematically studied for their effect on imbalanced problems. Among these are Random Oracles, Disturbing Neighbors, Random Feature Weights or Rotation Forest. This paper presents an overview and an experimental study of various ensemble-based methods for imbalanced problems, the methods have been tested in its original form and in conjunction with several diversity-increasing techniques, using 84 imbalanced data sets from two well known repositories. This paper shows that these diversity-increasing techniques significantly improve the performance of ensemble methods for imbalanced problems and provides some ideas about when it is more convenient to use these diversifying techniques. |
|---|---|
| AbstractList | Many real-life problems can be described as unbalanced, where the number of instances belonging to one of the classes is much larger than the numbers in other classes. Examples are spam detection, credit card fraud detection or medical diagnosis. Ensembles of classifiers have acquired popularity in this kind of problems for their ability to obtain better results than individual classifiers. The most commonly used techniques by those ensembles especially designed to deal with imbalanced problems are for example Re-weighting, Oversampling and Undersampling. Other techniques, originally intended to increase the ensemble diversity, have not been systematically studied for their effect on imbalanced problems. Among these are Random Oracles, Disturbing Neighbors, Random Feature Weights or Rotation Forest. This paper presents an overview and an experimental study of various ensemble-based methods for imbalanced problems, the methods have been tested in its original form and in conjunction with several diversity-increasing techniques, using 84 imbalanced data sets from two well known repositories. This paper shows that these diversity-increasing techniques significantly improve the performance of ensemble methods for imbalanced problems and provides some ideas about when it is more convenient to use these diversifying techniques. |
| Author | García-Osorio, César I. Kuncheva, Ludmila I. Díez-Pastor, José F. Rodríguez, Juan J. |
| Author_xml | – sequence: 1 givenname: José F. surname: Díez-Pastor fullname: Díez-Pastor, José F. email: jfdpastor@ubu.es organization: University of Burgos, Spain – sequence: 2 givenname: Juan J. surname: Rodríguez fullname: Rodríguez, Juan J. organization: University of Burgos, Spain – sequence: 3 givenname: César I. surname: García-Osorio fullname: García-Osorio, César I. organization: University of Burgos, Spain – sequence: 4 givenname: Ludmila I. surname: Kuncheva fullname: Kuncheva, Ludmila I. organization: University of Bangor, UK |
| BookMark | eNp9kD1PwzAURS0EEi3wA9gysiT4OY2diAnxLVViobPlOM_UVeIU263Uf4_bMjF0snR1z9P1mZJzNzok5BZoART4_aqwLhSMQlVQUVBWnZEJ1ILlnDVwTiaUMpqnuLok0xBWlNKZ4HxCFs92iz7YuMsi6qWzPxsMmR3WftxiFpeYrdGb0Q_KacxGc4haDDF1WtUf0h6Vd9Z9Z-gCDm2P4ZpcGNUHvPl7r8ji9eXr6T2ff759PD3Oc12WNOaATJVdUzI-UxXTvNRo6pKCaRqooG1rZjowpgTDwbRGqKYTqjOs0gK40l15Re6Od9Pc_fAoBxs09mkXjpsgQYiazqqm5qkKx6r2YwgejVx7Oyi_k0DlXqFcyaRQ7hVKKmRylRjxj9E2qmhHF72y_Uny4Uhi-v3WopdBW0yyOutRR9mN9gT9CyxHj5M |
| CitedBy_id | crossref_primary_10_1007_s12517_022_10243_x crossref_primary_10_3390_app8050815 crossref_primary_10_1016_j_eswa_2020_113920 crossref_primary_10_1016_j_ins_2020_08_069 crossref_primary_10_1007_s13748_019_00172_4 crossref_primary_10_1016_j_neucom_2021_08_086 crossref_primary_10_1016_j_asoc_2024_112186 crossref_primary_10_1016_j_jbi_2019_103124 crossref_primary_10_1016_j_jhydrol_2024_131658 crossref_primary_10_1007_s13042_020_01271_8 crossref_primary_10_1155_2016_5873769 crossref_primary_10_1007_s11063_024_11695_w crossref_primary_10_1016_j_ins_2016_12_026 crossref_primary_10_1007_s10044_022_01103_1 crossref_primary_10_1109_ACCESS_2024_3396155 crossref_primary_10_1109_TGRS_2022_3187751 crossref_primary_10_1016_j_engappai_2021_104355 crossref_primary_10_1016_j_inffus_2017_12_003 crossref_primary_10_1016_j_knosys_2017_08_022 crossref_primary_10_1016_j_aap_2019_05_005 crossref_primary_10_1016_j_patcog_2022_109158 crossref_primary_10_1007_s11634_019_00354_x crossref_primary_10_1016_j_petsci_2024_11_001 crossref_primary_10_1016_j_eswa_2019_113005 crossref_primary_10_1016_j_ijar_2025_109542 crossref_primary_10_1016_j_eswa_2021_116015 crossref_primary_10_1016_j_ins_2018_12_033 crossref_primary_10_1109_TITS_2022_3166838 crossref_primary_10_1155_2015_592549 crossref_primary_10_1016_j_knosys_2020_105845 crossref_primary_10_1109_TCPMT_2020_3047089 crossref_primary_10_1016_j_ins_2024_121103 crossref_primary_10_1007_s00521_021_06462_0 crossref_primary_10_1016_j_compbiolchem_2017_08_015 crossref_primary_10_1109_TNNLS_2022_3177695 crossref_primary_10_1007_s10489_017_1088_8 crossref_primary_10_7717_peerj_cs_76 crossref_primary_10_1007_s13042_015_0478_7 crossref_primary_10_1016_j_inffus_2021_03_007 crossref_primary_10_1016_j_inffus_2017_09_010 crossref_primary_10_3390_s20226699 crossref_primary_10_1109_ACCESS_2019_2917920 crossref_primary_10_1038_srep38660 crossref_primary_10_1109_TCYB_2021_3133106 crossref_primary_10_1016_j_neucom_2019_04_072 crossref_primary_10_3233_JIFS_210624 crossref_primary_10_1016_j_neucom_2024_128959 crossref_primary_10_1016_j_ins_2016_09_038 crossref_primary_10_1016_j_ins_2018_06_056 crossref_primary_10_1007_s10489_022_03590_5 crossref_primary_10_1109_TMI_2016_2527736 crossref_primary_10_1109_ACCESS_2025_3574677 crossref_primary_10_1016_j_ins_2020_03_027 crossref_primary_10_1016_j_ins_2021_12_066 crossref_primary_10_1016_j_ins_2017_04_044 crossref_primary_10_1016_j_ins_2020_12_023 crossref_primary_10_1109_TKDE_2024_3384274 crossref_primary_10_1016_j_isprsjprs_2017_04_017 crossref_primary_10_1016_j_knosys_2023_110745 crossref_primary_10_1016_j_patcog_2023_110107 crossref_primary_10_1109_TCE_2023_3319439 crossref_primary_10_1016_j_jbi_2017_03_002 crossref_primary_10_1007_s00521_024_10960_2 crossref_primary_10_1016_j_neucom_2016_08_071 crossref_primary_10_1016_j_procs_2019_09_167 crossref_primary_10_1007_s10115_025_02361_1 crossref_primary_10_1109_TNNLS_2021_3136503 crossref_primary_10_1007_s12065_024_00969_w crossref_primary_10_1007_s10462_024_10724_3 crossref_primary_10_1016_j_eswa_2023_121269 crossref_primary_10_1016_j_ins_2020_12_006 crossref_primary_10_3390_electronics11050745 crossref_primary_10_1016_j_ins_2020_01_032 crossref_primary_10_1109_ACCESS_2020_2979054 crossref_primary_10_1016_j_elerap_2018_10_004 crossref_primary_10_1371_journal_pone_0194852 crossref_primary_10_1111_exsy_12363 crossref_primary_10_1016_j_engappai_2019_103319 crossref_primary_10_1016_j_knosys_2020_106598 crossref_primary_10_1155_2016_8752181 crossref_primary_10_1016_j_knosys_2022_108410 crossref_primary_10_1109_TKDE_2020_2974949 crossref_primary_10_1007_s00521_016_2458_6 crossref_primary_10_1016_j_eswa_2018_05_017 crossref_primary_10_3390_app11188546 crossref_primary_10_3233_HIS_190261 crossref_primary_10_1007_s13042_019_01047_9 crossref_primary_10_1016_j_patcog_2018_07_037 crossref_primary_10_1016_j_neucom_2017_04_081 crossref_primary_10_1142_S0218126625502044 crossref_primary_10_1109_TKDE_2020_2985965 crossref_primary_10_1016_j_engappai_2024_108857 crossref_primary_10_1109_LGRS_2019_2913387 crossref_primary_10_1007_s42452_020_2039_2 crossref_primary_10_1109_TNNLS_2021_3071122 crossref_primary_10_1016_j_ins_2016_08_077 crossref_primary_10_1016_j_ins_2024_120699 crossref_primary_10_1016_j_knosys_2022_108919 crossref_primary_10_1016_j_eswa_2016_12_035 crossref_primary_10_1016_j_asoc_2019_03_056 crossref_primary_10_1016_j_asoc_2021_107447 crossref_primary_10_1016_j_knosys_2021_107588 crossref_primary_10_1016_j_eswa_2020_114317 crossref_primary_10_1016_j_eswa_2020_113504 crossref_primary_10_1190_geo2022_0782_1 crossref_primary_10_1007_s42979_020_0119_4 crossref_primary_10_1080_08839514_2021_1877481 crossref_primary_10_1007_s10044_022_01129_5 crossref_primary_10_1016_j_eswa_2015_10_031 crossref_primary_10_5194_hess_25_2543_2021 crossref_primary_10_1109_TITS_2022_3207798 crossref_primary_10_1016_j_aap_2021_106511 crossref_primary_10_1007_s10115_024_02129_z crossref_primary_10_1109_ACCESS_2017_2756102 crossref_primary_10_1111_ddi_13246 crossref_primary_10_1093_comjnl_bxab038 crossref_primary_10_1016_j_engappai_2020_103500 crossref_primary_10_1007_s00521_021_06066_8 crossref_primary_10_1016_j_neucom_2020_05_029 crossref_primary_10_1109_ACCESS_2024_3480848 crossref_primary_10_1007_s10994_024_06604_0 crossref_primary_10_1016_j_eswa_2023_119703 crossref_primary_10_3390_su11010196 crossref_primary_10_1016_j_compbiomed_2022_105351 crossref_primary_10_1186_s42400_024_00290_0 crossref_primary_10_3390_info12080291 crossref_primary_10_1016_j_neucom_2024_128372 crossref_primary_10_1016_j_neucom_2018_01_060 |
| Cites_doi | 10.1007/s10044-003-0192-z 10.1109/TSMCA.2009.2029559 10.1145/1007730.1007735 10.1016/j.patcog.2013.05.006 10.1016/j.patrec.2005.10.010 10.1109/TKDE.2007.1016 10.1109/TST.2012.6374368 10.1109/TSMCC.2011.2161285 10.1145/1007730.1007737 10.1007/BF00058655 10.1109/TSMC.1976.4309452 10.1080/03610928008827904 10.1023/A:1007515423169 10.1016/j.inffus.2010.11.004 10.1016/j.ins.2011.06.023 10.1109/34.709601 10.1016/j.ins.2013.07.007 10.1023/A:1007659514849 10.1093/biomet/75.4.800 10.1023/A:1024099825458 10.1145/1007730.1007733 10.1016/j.knosys.2011.05.002 10.1613/jair.606 10.1007/s10618-011-0222-1 10.1613/jair.953 10.1109/34.990132 10.1023/A:1010933404324 10.1162/089976698300017197 10.1016/j.eswa.2010.06.072 10.1023/A:1009876119989 10.1109/TR.2013.2259203 10.1109/TSMC.1972.4309137 10.1109/TPAMI.2006.211 10.1145/1656274.1656278 10.1016/j.knosys.2015.04.022 10.1109/TKDE.2009.187 10.1109/TNN.2010.2066988 |
| ContentType | Journal Article |
| Copyright | 2015 Elsevier Inc. |
| Copyright_xml | – notice: 2015 Elsevier Inc. |
| DBID | AAYXX CITATION 7SC 8FD JQ2 L7M L~C L~D |
| DOI | 10.1016/j.ins.2015.07.025 |
| DatabaseName | CrossRef Computer and Information Systems Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional |
| DatabaseTitle | CrossRef Computer and Information Systems Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Advanced Technologies Database with Aerospace ProQuest Computer Science Collection Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | Computer and Information Systems Abstracts |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Library & Information Science |
| EISSN | 1872-6291 |
| EndPage | 117 |
| ExternalDocumentID | 10_1016_j_ins_2015_07_025 S0020025515005186 |
| GroupedDBID | --K --M --Z -~X .DC .~1 0R~ 1B1 1RT 1~. 1~5 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN 9JO AAAKF AABNK AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AARIN AAXUO AAYFN ABAOU ABBOA ABFNM ABJNI ABMAC ABUCO ABYKQ ACAZW ACDAQ ACGFS ACRLP ACZNC ADBBV ADEZE ADGUI ADTZH AEBSH AECPX AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIGVJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD APLSM ARUGR AXJTR BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ HAMUX IHE J1W JJJVA KOM LG9 LY1 M41 MHUIS MO0 MS~ N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 RIG ROL RPZ SDF SDG SDP SES SPC SPCBC SSB SSD SST SSV SSW SSZ T5K TN5 TWZ WH7 XPP ZMT ~02 ~G- 1OL 29I 77I 9DU AAAKG AAQXK AATTM AAXKI AAYWO AAYXX ABEFU ABWVN ABXDB ACLOT ACNNM ACRPL ACVFH ADCNI ADJOM ADMUD ADNMO ADVLN AEIPS AEUPX AFFNX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP ASPBG AVWKF AZFZN CITATION EFKBS FEDTE FGOYB HLZ HVGLF HZ~ H~9 R2- SBC SDS SEW UHS WUQ YYP ZY4 ~HD 7SC 8FD JQ2 L7M L~C L~D |
| ID | FETCH-LOGICAL-c330t-1e2a3d93264a52c63cef8301f99151bb82fd1ff31f61fbf7a9d7adf25c716acd3 |
| ISICitedReferencesCount | 155 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000362380600007&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0020-0255 |
| IngestDate | Thu Oct 02 10:29:47 EDT 2025 Tue Nov 18 19:49:11 EST 2025 Sat Nov 29 06:24:58 EST 2025 Fri Feb 23 02:33:58 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Undersampling Rotation forest SMOTE Imbalanced data sets Diversity Classifier ensembles |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c330t-1e2a3d93264a52c63cef8301f99151bb82fd1ff31f61fbf7a9d7adf25c716acd3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| PQID | 1778045986 |
| PQPubID | 23500 |
| PageCount | 20 |
| ParticipantIDs | proquest_miscellaneous_1778045986 crossref_primary_10_1016_j_ins_2015_07_025 crossref_citationtrail_10_1016_j_ins_2015_07_025 elsevier_sciencedirect_doi_10_1016_j_ins_2015_07_025 |
| PublicationCentury | 2000 |
| PublicationDate | 2015-12-20 |
| PublicationDateYYYYMMDD | 2015-12-20 |
| PublicationDate_xml | – month: 12 year: 2015 text: 2015-12-20 day: 20 |
| PublicationDecade | 2010 |
| PublicationTitle | Information sciences |
| PublicationYear | 2015 |
| Publisher | Elsevier Inc |
| Publisher_xml | – name: Elsevier Inc |
| References | García-Pedrajas, Pérez-Rodríguez, García-Pedrajas, Ortiz-Boyer, Fyfe (bib0028) 2012; 25 Sun, Kamel, Wong, Wang (bib0060) 2007; volume 40 C. Van Rijsbergen, Information Retrieval, 1979, Butterworths. Hall, Frank, Holmes, Pfahringer, Reutemann, Witten (bib0030) Nov. 2009; 11 Chawla, Bowyer, Hall, Kegelmeyer (bib0010) 2002; 16 Hochberg (bib0036) 1988; 75 Fan, Stolfo, Zhang, Chan (bib0020) 1999 Galar, Fernandez, Barrenechea, Bustince, Herrera (bib0023) july 2012; 42 Cieslak, Chawla (bib0014) 2008 Cieslak, Hoens, Chawla, Kegelmeyer (bib0015) Jan. 2012; 24 K. Bache, M. Lichman, UCI machine learning repository. 2013 Dietterich (bib0018) 1998; 10 Quionero-Candela, Sugiyama, Schwaighofer, Lawrence (bib0054) 2009 He, Bai, Garcia, Li (bib0032) 2008 Batista, Prati, Monard (bib0004) 2004; 6 Rodriguez, Kuncheva, Alonso (bib0055) 2006; 28 Yu, Ni, Dan, Xu (bib0070) 2012; 17 Brodley, Friedl (bib0008) 1999; 11 Chen, He, Garcia (bib0013) 2010; 21 Kubat, Matwin (bib0039) 1997 Wang, Yao (bib0064) 2009 García, Marqués, Sánchez (bib0025) 2012 López, Fernández, García, Palade, Herrera (bib0044) 2013; 250 Alcala-Fdez, Fernández, Luengo, Derrac, García, Sánchez, Herrera (bib0001) 2011; 17 . Iman, Davenport (bib0037) 1980; 9 Liu, Wu, Zhou (bib0043) 2009; 39 Napierała, Stefanowski, Wilk (bib0047) 2010 Fawcett (bib0021) 2006; 27 Schclar, Rokach (bib0056) 2009 Wilson (bib0069) 1972; 2 Han, Wang, Mao (bib0031) 2005 Maudes, Rodríguez, García-Osorio, García-Pedrajas (bib0046) 2012; 13 Ho, Basu (bib0034) 2002; 24 Barandela, Valdovinos, Sánchez (bib0003) 2003; 6 Kuncheva (bib0040) 2004 Seiffert, Khoshgoftaar, Van Hulse, Napolitano (bib0057) 2010; 40 Provost, Domingos (bib0051) 2003; 52 Demšar (bib0016) 2006; 7 Polikar (bib0049) 2012 Ho, Basu, Law (bib0035) 2006 Breiman (bib0006) 1996; 24 Chawla, Lazarevic, Hall, Bowyer (bib0012) 2003 Kuncheva, Rodriguez (bib0041) 2007; 19 Sofia (bib0058) 2005 Stefanowski (bib0059) 2013 García-Pedrajas, García-Osorio (bib0026) 2011; 38 Chawla, Japkowicz, Kotcz (bib0011) 2004; 6 Bauer, Kohavi (bib0005) 1999; 36 Prati, Batista, Silva (bib0050) 2014 Díez-Pastor, Rodríguez, García-Osorio, Kuncheva (bib0019) 2015; 85 Galar, Fernández, Barrenechea, Herrera (bib0024) 2013; 46 I. Tomek, Two modifications of cnn. systems, man and cybernetics, transactions on 6, 1976, 769–772. Freund, Schapire (bib0022) July 3--6, 1996 Liu, Chawla, Cieslak, Chawla (bib0042) 2010 Quinlan (bib0053) 1993 Zadrozny, Elkan (bib0071) 2001 Jo, Japkowicz (bib0038) 2004; 6 Geng, Wang, Li, Xu, Jin (bib0029) 2007; Vol. 4 Webb (bib0067) 2000; 40 Bunkhumpornpat, Sinapiromsaran, Lursinsap (bib0009) 2009 Breiman (bib0007) 2001; 45 Ho (bib0033) Aug 1998; 20 Provost, Kolluri (bib0052) 1999; 3 Maudes, Rodríguez, García-Osorio (bib0045) 2009 García-Pedrajas, Maudes-Raedo, García-Osorio, Rodríguez-Díez (bib0027) 2012; 193 Wang, Yao (bib0065) June 2013; 62 Orriols-Puig, Macià, Ho (bib0048) 2010 Wasikowski, wen Chen (bib0066) Oct 2010; 22 Verhein, Chawla (bib0063) Oct 2007 Di Martino, Decia, Molinelli, Fernández (bib0017) 2012; volume 2 Weiss (bib0068) 2010 Barandela (10.1016/j.ins.2015.07.025_bib0003) 2003; 6 Sun (10.1016/j.ins.2015.07.025_bib0060) 2007; volume 40 García (10.1016/j.ins.2015.07.025_bib0025) 2012 Provost (10.1016/j.ins.2015.07.025_bib0052) 1999; 3 Prati (10.1016/j.ins.2015.07.025_bib0050) 2014 García-Pedrajas (10.1016/j.ins.2015.07.025_bib0028) 2012; 25 Wasikowski (10.1016/j.ins.2015.07.025_bib0066) 2010; 22 Napierała (10.1016/j.ins.2015.07.025_bib0047) 2010 Kubat (10.1016/j.ins.2015.07.025_bib0039) 1997 Brodley (10.1016/j.ins.2015.07.025_bib0008) 1999; 11 Liu (10.1016/j.ins.2015.07.025_bib0043) 2009; 39 Stefanowski (10.1016/j.ins.2015.07.025_bib0059) 2013 Zadrozny (10.1016/j.ins.2015.07.025_bib0071) 2001 Chen (10.1016/j.ins.2015.07.025_bib0013) 2010; 21 Chawla (10.1016/j.ins.2015.07.025_bib0012) 2003 Dietterich (10.1016/j.ins.2015.07.025_bib0018) 1998; 10 García-Pedrajas (10.1016/j.ins.2015.07.025_bib0026) 2011; 38 Geng (10.1016/j.ins.2015.07.025_bib0029) 2007; Vol. 4 Wang (10.1016/j.ins.2015.07.025_bib0064) 2009 Hochberg (10.1016/j.ins.2015.07.025_bib0036) 1988; 75 Ho (10.1016/j.ins.2015.07.025_bib0033) 1998; 20 Jo (10.1016/j.ins.2015.07.025_bib0038) 2004; 6 10.1016/j.ins.2015.07.025_bib0061 Orriols-Puig (10.1016/j.ins.2015.07.025_bib0048) 2010 Wilson (10.1016/j.ins.2015.07.025_bib0069) 1972; 2 10.1016/j.ins.2015.07.025_bib0062 Chawla (10.1016/j.ins.2015.07.025_bib0011) 2004; 6 Ho (10.1016/j.ins.2015.07.025_bib0035) 2006 Yu (10.1016/j.ins.2015.07.025_bib0070) 2012; 17 Sofia (10.1016/j.ins.2015.07.025_bib0058) 2005 Galar (10.1016/j.ins.2015.07.025_bib0024) 2013; 46 Seiffert (10.1016/j.ins.2015.07.025_bib0057) 2010; 40 Di Martino (10.1016/j.ins.2015.07.025_bib0017) 2012; volume 2 Hall (10.1016/j.ins.2015.07.025_bib0030) 2009; 11 García-Pedrajas (10.1016/j.ins.2015.07.025_bib0027) 2012; 193 Polikar (10.1016/j.ins.2015.07.025_bib0049) 2012 Rodriguez (10.1016/j.ins.2015.07.025_bib0055) 2006; 28 Freund (10.1016/j.ins.2015.07.025_bib0022) 1996 Chawla (10.1016/j.ins.2015.07.025_bib0010) 2002; 16 Bauer (10.1016/j.ins.2015.07.025_bib0005) 1999; 36 Cieslak (10.1016/j.ins.2015.07.025_bib0014) 2008 Quinlan (10.1016/j.ins.2015.07.025_bib0053) 1993 Wang (10.1016/j.ins.2015.07.025_bib0065) 2013; 62 Fawcett (10.1016/j.ins.2015.07.025_bib0021) 2006; 27 Verhein (10.1016/j.ins.2015.07.025_bib0063) 2007 Kuncheva (10.1016/j.ins.2015.07.025_bib0040) 2004 Díez-Pastor (10.1016/j.ins.2015.07.025_bib0019) 2015; 85 He (10.1016/j.ins.2015.07.025_bib0032) 2008 López (10.1016/j.ins.2015.07.025_bib0044) 2013; 250 Liu (10.1016/j.ins.2015.07.025_bib0042) 2010 Weiss (10.1016/j.ins.2015.07.025_bib0068) 2010 Provost (10.1016/j.ins.2015.07.025_bib0051) 2003; 52 Ho (10.1016/j.ins.2015.07.025_bib0034) 2002; 24 Webb (10.1016/j.ins.2015.07.025_bib0067) 2000; 40 Alcala-Fdez (10.1016/j.ins.2015.07.025_bib0001) 2011; 17 Bunkhumpornpat (10.1016/j.ins.2015.07.025_bib0009) 2009 Schclar (10.1016/j.ins.2015.07.025_bib0056) 2009 Batista (10.1016/j.ins.2015.07.025_bib0004) 2004; 6 Breiman (10.1016/j.ins.2015.07.025_bib0006) 1996; 24 Cieslak (10.1016/j.ins.2015.07.025_bib0015) 2012; 24 Fan (10.1016/j.ins.2015.07.025_bib0020) 1999 Maudes (10.1016/j.ins.2015.07.025_bib0045) 2009 Han (10.1016/j.ins.2015.07.025_bib0031) 2005 Demšar (10.1016/j.ins.2015.07.025_bib0016) 2006; 7 Kuncheva (10.1016/j.ins.2015.07.025_bib0041) 2007; 19 Maudes (10.1016/j.ins.2015.07.025_bib0046) 2012; 13 10.1016/j.ins.2015.07.025_bib0002 Iman (10.1016/j.ins.2015.07.025_bib0037) 1980; 9 Breiman (10.1016/j.ins.2015.07.025_bib0007) 2001; 45 Galar (10.1016/j.ins.2015.07.025_bib0023) 2012; 42 Quionero-Candela (10.1016/j.ins.2015.07.025_bib0054) 2009 |
| References_xml | – volume: 85 start-page: 96 year: 2015 end-page: 111 ident: bib0019 article-title: Random balance: Ensembles of variable priors classiffiers for imbalanced data publication-title: Knowledge-Based Systems – start-page: 1322 year: 2008 end-page: 1328 ident: bib0032 article-title: Adasyn: adaptive synthetic sampling approach for imbalanced learning publication-title: IEEE International Joint Conference on. Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence) – start-page: 309 year: 2009 end-page: 316 ident: bib0056 article-title: Random projection ensemble classifiers publication-title: Enterprise Information Systems – volume: 2 start-page: 408 year: 1972 end-page: 421 ident: bib0069 article-title: Asymptotic properties of nearest neighbor rules using edited data publication-title: IEEE Transactions on Syst. Man Cybernetics – start-page: 97 year: 1999 end-page: 105 ident: bib0020 article-title: AdaCost: misclassification cost-sensitive boosting publication-title: Proceedings of the Sixteenth International Conference on Machine Learning. ICML ’99 – start-page: 148 year: July 3--6, 1996 end-page: 156 ident: bib0022 article-title: Experiments with a new boosting algorithm publication-title: Machine Learning, Proceedings of the Thirteenth International Conference (ICML ’96), Bari, Italy – volume: volume 40 start-page: 3358 year: 2007 end-page: 3378 ident: bib0060 article-title: Cost-sensitive boosting for classification of imbalanced data publication-title: Pattern Recognition – start-page: 766 year: 2010 end-page: 777 ident: bib0042 article-title: A robust decision tree algorithm for imbalanced data sets publication-title: Proceedings of the SIAM International Conference on Data Mining, SDM 2010 – year: 2010 ident: bib0048 article-title: Documentation for the data complexity library in C++ publication-title: Tech. rep. – volume: 24 start-page: 123 year: 1996 end-page: 140 ident: bib0006 article-title: Bagging predictors publication-title: Machine Learn. – volume: 6 start-page: 245 year: 2003 end-page: 256 ident: bib0003 article-title: New applications of ensembles of classifiers publication-title: Pattern Anal. Appl. – volume: 9 start-page: 571 year: 1980 end-page: 595 ident: bib0037 article-title: Approximations of the critical region of the fbietkan statistic publication-title: Commun. Stat. Theory Method. – start-page: 277 year: 2013 end-page: 306 ident: bib0059 article-title: Overlapping, rare examples and class decomposition in learning classifiers from imbalanced data publication-title: Emerging Paradigms in Machine Learning – volume: 193 start-page: 1 year: 2012 end-page: 21 ident: bib0027 article-title: Supervised subspace projections for constructing ensembles of classifiers publication-title: Inf. Sci. – year: 1993 ident: bib0053 publication-title: C4.5: Programs for Machine Learning – volume: 7 start-page: 1 year: 2006 end-page: 30 ident: bib0016 article-title: Statistical comparisons of classifiers over multiple data sets publication-title: J. Mach. Learn. Res. – volume: 42 start-page: 463 year: july 2012 end-page: 484 ident: bib0023 article-title: A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches publication-title: Syst. Man Cybernetics Part C: appl. Rev. IEEE Transactions on – volume: 6 start-page: 20 year: 2004 end-page: 29 ident: bib0004 article-title: A study of the behavior of several methods for balancing machine learning training data publication-title: ACM SIGKDD Explor. Newslett. – volume: Vol. 4 start-page: 583 year: 2007 end-page: 587 ident: bib0029 article-title: Boosting the performance of web spam detection with ensemble under-sampling classification publication-title: 2007. FSKD 2007. Fourth International Conference on, Fuzzy Systems and Knowledge Discovery – volume: 11 start-page: 10 year: Nov. 2009 end-page: 18 ident: bib0030 article-title: The WEKA data mining software: an update publication-title: ACM SIGKDD Explor. Newslett. – volume: 250 start-page: 113 year: 2013 end-page: 141 ident: bib0044 article-title: An insight into classification with imbalanced data: empirical results and current trends on using data intrinsic characteristics publication-title: Inf. Sci. – start-page: 324 year: 2009 end-page: 331 ident: bib0064 article-title: Diversity analysis on imbalanced data sets by using ensemble models publication-title: IEEE Symposium Series on Computational Intelligence and Data Mining (IEEE CIDM 2009) – volume: 21 start-page: 1624 year: 2010 end-page: 1642 ident: bib0013 article-title: Ramoboost: ranked minority oversampling in boosting publication-title: Neural Net. IEEE Transactions on – year: 2009 ident: bib0054 publication-title: Dataset Shift in Machine Learning – volume: 52 start-page: 199 year: 2003 end-page: 215 ident: bib0051 article-title: Tree induction for probability-based ranking publication-title: Machine Learn. – start-page: 193 year: 2010 end-page: 226 ident: bib0068 article-title: The impact of small disjuncts on classifier learning publication-title: Data Mining – reference: I. Tomek, Two modifications of cnn. systems, man and cybernetics, transactions on 6, 1976, 769–772. – start-page: 67 year: 2005 end-page: 73 ident: bib0058 article-title: Issues in mining imbalanced data sets — a review paper publication-title: Proceedings of the Sixteen Midwest Artificial Intelligence and Cognitive Science Conference – volume: 27 start-page: 861 year: 2006 end-page: 874 ident: bib0021 article-title: An introduction to ROC analysis publication-title: Pattern recognit. lett. – volume: 10 start-page: 1895 year: 1998 end-page: 1923 ident: bib0018 article-title: Approximate statistical tests for comparing supervised classification learning algorithms publication-title: Neural comput. – start-page: 1 year: 2012 end-page: 34 ident: bib0049 article-title: Ensemble learning publication-title: Ensemble Machine Learning – volume: 17 start-page: 255 year: 2011 end-page: 287 ident: bib0001 article-title: KEEL data-mining software tool: data set repository and integration of algorithms and experimental analysis framework publication-title: J. Multiple-Valued Logic Soft Comput. – start-page: 179 year: 1997 end-page: 186 ident: bib0039 article-title: Addressing the Curse of imbalanced training sets: one-Sided selection publication-title: Proceedings of the 14th International Conference on Machine Learning – year: 2004 ident: bib0040 publication-title: Combining Pattern Classifiers: Methods and Algorithms – start-page: 679 year: Oct 2007 end-page: 684 ident: bib0063 article-title: Using significant, positively associated and relatively class correlated rules for associative classification of imbalanced datasets publication-title: Seventh IEEE International Conference on Data Mining, 2007. ICDM 2007 – volume: 17 start-page: 666 year: 2012 end-page: 673 ident: bib0070 article-title: Mining and integrating reliable decision rules for imbalanced cancer gene expression data sets publication-title: Tsinghua Sci. Tech. – volume: 22 start-page: 1388 year: Oct 2010 end-page: 1400 ident: bib0066 article-title: Combating the small sample class imbalance problem using feature selection publication-title: Knowledge Data Eng. IEEE Transactions on – volume: 3 start-page: 131 year: 1999 end-page: 169 ident: bib0052 article-title: A survey of methods for scaling up inductive algorithms publication-title: Data min. knowledge discovery – volume: 36 start-page: 105 year: 1999 end-page: 139 ident: bib0005 article-title: An empirical comparison of voting classification algorithms: Bagging, boosting, and variants publication-title: Machine learn. – start-page: 107 year: 2003 end-page: 119 ident: bib0012 article-title: SMOTEBoost: improving prediction of the minority class in boosting publication-title: 7th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD 2003) – start-page: 68 year: 2012 end-page: 75 ident: bib0025 article-title: Improving risk predictions by preprocessing imbalanced credit data publication-title: Neural Information Processing – volume: 39 start-page: 539 year: 2009 end-page: 550 ident: bib0043 article-title: Exploratory undersampling for class-imbalance learning. systems, man, and cybernetics publication-title: IEEE Transactions on Part B: Cybernetics – volume: 19 start-page: 500 year: 2007 end-page: 508 ident: bib0041 article-title: Classifier ensembles with a random linear oracle publication-title: IEEE Transactions on Knowledge Data Eng. – start-page: 1 year: 2006 end-page: 23 ident: bib0035 article-title: Measures of geometrical complexity in classification problems publication-title: Data complexity in pattern recognition – volume: 28 start-page: 1619 year: 2006 end-page: 1630 ident: bib0055 article-title: Rotation forest: a new classifier ensemble method publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence – start-page: 878 year: 2005 end-page: 887 ident: bib0031 article-title: Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning publication-title: 2005 International Conference on Intelligent Computing (ICIC05) – volume: 6 start-page: 1 year: 2004 end-page: 6 ident: bib0011 article-title: Editorial: special issue on learning from imbalanced data sets publication-title: ACM SIGKDD Explor. Newslett. – volume: 6 start-page: 40 year: 2004 end-page: 49 ident: bib0038 article-title: Class imbalances versus small disjuncts publication-title: ACM SIGKDD Explor. Newslett. – volume: 13 start-page: 20 year: 2012 end-page: 30 ident: bib0046 article-title: Random feature weights for decision tree ensemble construction publication-title: Inf. Fusion – start-page: 241 year: 2008 end-page: 256 ident: bib0014 article-title: Learning decision trees for unbalanced data publication-title: Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I. ECML PKDD ’08 – start-page: 204 year: 2001 end-page: 213 ident: bib0071 article-title: Learning and making decisions when costs and probabilities are both unknown publication-title: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining – start-page: 1 year: 2014 end-page: 24 ident: bib0050 article-title: Class imbalance revisited: a new experimental setup to assess the performance of treatment methods publication-title: Knowledge and Information Systems – start-page: 158 year: 2010 end-page: 167 ident: bib0047 article-title: Learning from imbalanced data in presence of noisy and borderline examples publication-title: Rough Sets and Current Trends in Computing – volume: 46 start-page: 3460 year: 2013 end-page: 3471 ident: bib0024 article-title: Eusboost: enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling publication-title: Pattern Recognit. – volume: 11 start-page: 131 year: 1999 end-page: 167 ident: bib0008 article-title: Identifying mislabeled training data publication-title: J. Artif. Intell. Res. – volume: volume 2 start-page: 135 year: 2012 end-page: 141 ident: bib0017 article-title: Improving electric fraud detection using class imbalance strategies publication-title: ICPRAM – volume: 24 start-page: 289 year: 2002 end-page: 300 ident: bib0034 article-title: Complexity measures of supervised classification problems publication-title: IEEE Transactions on Pattern Anal. Mach. Intell. – start-page: 475 year: 2009 end-page: 482 ident: bib0009 article-title: Safe-level-SMOTE: Safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem publication-title: Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD09) – volume: 20 start-page: 832 year: Aug 1998 end-page: 844 ident: bib0033 article-title: The random subspace method for constructing decision forests publication-title: IEEE Transactions on Pattern Anal. Mach. Intell. – volume: 24 start-page: 136 year: Jan. 2012 end-page: 158 ident: bib0015 article-title: Hellinger distance decision trees are robust and skew-insensitive publication-title: Data Min. Know. Discovery – reference: . – start-page: 113 year: 2009 end-page: 133 ident: bib0045 article-title: Disturbing neighbors diversity for decision forests publication-title: Applications of Supervised and Unsupervised Ensemble Methods – reference: K. Bache, M. Lichman, UCI machine learning repository. 2013, – reference: C. Van Rijsbergen, Information Retrieval, 1979, Butterworths. – volume: 38 start-page: 343 year: 2011 end-page: 359 ident: bib0026 article-title: Constructing ensembles of classifiers using supervised projection methods based on misclassified instances publication-title: Expert Syst. Appl. – volume: 45 start-page: 5 year: 2001 end-page: 32 ident: bib0007 article-title: Random forests publication-title: Machine learn. – volume: 75 start-page: 800 year: 1988 end-page: 803 ident: bib0036 article-title: A sharper Bonferroni procedure for multiple tests of significance publication-title: Biometrika – volume: 40 start-page: 159 year: 2000 end-page: 196 ident: bib0067 article-title: Multiboosting: A technique for combining boosting and wagging publication-title: Machine Learn. – volume: 62 start-page: 434 year: June 2013 end-page: 443 ident: bib0065 article-title: Using class imbalance learning for software defect prediction publication-title: IEEE Transactions on Reliability – volume: 25 start-page: 22 year: 2012 end-page: 34 ident: bib0028 article-title: Class imbalance methods for translation initiation site recognition in DNA sequences publication-title: Knowledge-Based Sys. – volume: 16 start-page: 321 year: 2002 end-page: 357 ident: bib0010 article-title: SMOTE: synthetic minority over-sampling technique publication-title: J. Artif. Intell. Res. – volume: 40 start-page: 185 year: 2010 end-page: 197 ident: bib0057 article-title: RUSBoost: A hybrid approach to alleviating class imbalance publication-title: IEEE Transactions on Syst. Man Cybernetics Part A: Syst. Humans – volume: 6 start-page: 245 issue: 3 year: 2003 ident: 10.1016/j.ins.2015.07.025_bib0003 article-title: New applications of ensembles of classifiers publication-title: Pattern Anal. Appl. doi: 10.1007/s10044-003-0192-z – volume: 40 start-page: 185 issue: 1 year: 2010 ident: 10.1016/j.ins.2015.07.025_bib0057 article-title: RUSBoost: A hybrid approach to alleviating class imbalance publication-title: IEEE Transactions on Syst. Man Cybernetics Part A: Syst. Humans doi: 10.1109/TSMCA.2009.2029559 – volume: 6 start-page: 20 issue: 1 year: 2004 ident: 10.1016/j.ins.2015.07.025_bib0004 article-title: A study of the behavior of several methods for balancing machine learning training data publication-title: ACM SIGKDD Explor. Newslett. doi: 10.1145/1007730.1007735 – volume: 46 start-page: 3460 issue: 12 year: 2013 ident: 10.1016/j.ins.2015.07.025_bib0024 article-title: Eusboost: enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2013.05.006 – start-page: 67 year: 2005 ident: 10.1016/j.ins.2015.07.025_bib0058 article-title: Issues in mining imbalanced data sets — a review paper – start-page: 766 year: 2010 ident: 10.1016/j.ins.2015.07.025_bib0042 article-title: A robust decision tree algorithm for imbalanced data sets – volume: 27 start-page: 861 issue: 8 year: 2006 ident: 10.1016/j.ins.2015.07.025_bib0021 article-title: An introduction to ROC analysis publication-title: Pattern recognit. lett. doi: 10.1016/j.patrec.2005.10.010 – volume: volume 40 start-page: 3358 year: 2007 ident: 10.1016/j.ins.2015.07.025_bib0060 article-title: Cost-sensitive boosting for classification of imbalanced data – volume: 19 start-page: 500 issue: 4 year: 2007 ident: 10.1016/j.ins.2015.07.025_bib0041 article-title: Classifier ensembles with a random linear oracle publication-title: IEEE Transactions on Knowledge Data Eng. doi: 10.1109/TKDE.2007.1016 – start-page: 113 year: 2009 ident: 10.1016/j.ins.2015.07.025_bib0045 article-title: Disturbing neighbors diversity for decision forests – volume: 39 start-page: 539 issue: 2 year: 2009 ident: 10.1016/j.ins.2015.07.025_bib0043 article-title: Exploratory undersampling for class-imbalance learning. systems, man, and cybernetics publication-title: IEEE Transactions on Part B: Cybernetics – volume: 17 start-page: 666 issue: 6 year: 2012 ident: 10.1016/j.ins.2015.07.025_bib0070 article-title: Mining and integrating reliable decision rules for imbalanced cancer gene expression data sets publication-title: Tsinghua Sci. Tech. doi: 10.1109/TST.2012.6374368 – volume: 42 start-page: 463 issue: 4 year: 2012 ident: 10.1016/j.ins.2015.07.025_bib0023 article-title: A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches publication-title: Syst. Man Cybernetics Part C: appl. Rev. IEEE Transactions on doi: 10.1109/TSMCC.2011.2161285 – volume: 6 start-page: 40 issue: 1 year: 2004 ident: 10.1016/j.ins.2015.07.025_bib0038 article-title: Class imbalances versus small disjuncts publication-title: ACM SIGKDD Explor. Newslett. doi: 10.1145/1007730.1007737 – start-page: 179 year: 1997 ident: 10.1016/j.ins.2015.07.025_bib0039 article-title: Addressing the Curse of imbalanced training sets: one-Sided selection – start-page: 158 year: 2010 ident: 10.1016/j.ins.2015.07.025_bib0047 article-title: Learning from imbalanced data in presence of noisy and borderline examples – start-page: 241 year: 2008 ident: 10.1016/j.ins.2015.07.025_bib0014 article-title: Learning decision trees for unbalanced data – start-page: 679 year: 2007 ident: 10.1016/j.ins.2015.07.025_bib0063 article-title: Using significant, positively associated and relatively class correlated rules for associative classification of imbalanced datasets – volume: 17 start-page: 255 issue: 2-3 year: 2011 ident: 10.1016/j.ins.2015.07.025_bib0001 article-title: KEEL data-mining software tool: data set repository and integration of algorithms and experimental analysis framework publication-title: J. Multiple-Valued Logic Soft Comput. – volume: 24 start-page: 123 year: 1996 ident: 10.1016/j.ins.2015.07.025_bib0006 article-title: Bagging predictors publication-title: Machine Learn. doi: 10.1007/BF00058655 – volume: 7 start-page: 1 year: 2006 ident: 10.1016/j.ins.2015.07.025_bib0016 article-title: Statistical comparisons of classifiers over multiple data sets publication-title: J. Mach. Learn. Res. – ident: 10.1016/j.ins.2015.07.025_bib0061 doi: 10.1109/TSMC.1976.4309452 – start-page: 475 year: 2009 ident: 10.1016/j.ins.2015.07.025_bib0009 article-title: Safe-level-SMOTE: Safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem – volume: volume 2 start-page: 135 year: 2012 ident: 10.1016/j.ins.2015.07.025_bib0017 article-title: Improving electric fraud detection using class imbalance strategies – volume: 9 start-page: 571 issue: 6 year: 1980 ident: 10.1016/j.ins.2015.07.025_bib0037 article-title: Approximations of the critical region of the fbietkan statistic publication-title: Commun. Stat. Theory Method. doi: 10.1080/03610928008827904 – volume: 36 start-page: 105 issue: 1--2 year: 1999 ident: 10.1016/j.ins.2015.07.025_bib0005 article-title: An empirical comparison of voting classification algorithms: Bagging, boosting, and variants publication-title: Machine learn. doi: 10.1023/A:1007515423169 – start-page: 1322 year: 2008 ident: 10.1016/j.ins.2015.07.025_bib0032 article-title: Adasyn: adaptive synthetic sampling approach for imbalanced learning – start-page: 1 year: 2012 ident: 10.1016/j.ins.2015.07.025_bib0049 article-title: Ensemble learning – start-page: 193 year: 2010 ident: 10.1016/j.ins.2015.07.025_bib0068 article-title: The impact of small disjuncts on classifier learning – volume: 13 start-page: 20 issue: 1 year: 2012 ident: 10.1016/j.ins.2015.07.025_bib0046 article-title: Random feature weights for decision tree ensemble construction publication-title: Inf. Fusion doi: 10.1016/j.inffus.2010.11.004 – start-page: 107 year: 2003 ident: 10.1016/j.ins.2015.07.025_bib0012 article-title: SMOTEBoost: improving prediction of the minority class in boosting – volume: 193 start-page: 1 issue: 0 year: 2012 ident: 10.1016/j.ins.2015.07.025_bib0027 article-title: Supervised subspace projections for constructing ensembles of classifiers publication-title: Inf. Sci. doi: 10.1016/j.ins.2011.06.023 – volume: 20 start-page: 832 issue: 8 year: 1998 ident: 10.1016/j.ins.2015.07.025_bib0033 article-title: The random subspace method for constructing decision forests publication-title: IEEE Transactions on Pattern Anal. Mach. Intell. doi: 10.1109/34.709601 – volume: Vol. 4 start-page: 583 year: 2007 ident: 10.1016/j.ins.2015.07.025_bib0029 article-title: Boosting the performance of web spam detection with ensemble under-sampling classification – volume: 250 start-page: 113 year: 2013 ident: 10.1016/j.ins.2015.07.025_bib0044 article-title: An insight into classification with imbalanced data: empirical results and current trends on using data intrinsic characteristics publication-title: Inf. Sci. doi: 10.1016/j.ins.2013.07.007 – volume: 40 start-page: 159 issue: 2 year: 2000 ident: 10.1016/j.ins.2015.07.025_bib0067 article-title: Multiboosting: A technique for combining boosting and wagging publication-title: Machine Learn. doi: 10.1023/A:1007659514849 – volume: 75 start-page: 800 year: 1988 ident: 10.1016/j.ins.2015.07.025_bib0036 article-title: A sharper Bonferroni procedure for multiple tests of significance publication-title: Biometrika doi: 10.1093/biomet/75.4.800 – volume: 52 start-page: 199 issue: 3 year: 2003 ident: 10.1016/j.ins.2015.07.025_bib0051 article-title: Tree induction for probability-based ranking publication-title: Machine Learn. doi: 10.1023/A:1024099825458 – volume: 6 start-page: 1 issue: 1 year: 2004 ident: 10.1016/j.ins.2015.07.025_bib0011 article-title: Editorial: special issue on learning from imbalanced data sets publication-title: ACM SIGKDD Explor. Newslett. doi: 10.1145/1007730.1007733 – volume: 25 start-page: 22 issue: 1 year: 2012 ident: 10.1016/j.ins.2015.07.025_bib0028 article-title: Class imbalance methods for translation initiation site recognition in DNA sequences publication-title: Knowledge-Based Sys. doi: 10.1016/j.knosys.2011.05.002 – start-page: 309 year: 2009 ident: 10.1016/j.ins.2015.07.025_bib0056 article-title: Random projection ensemble classifiers – start-page: 68 year: 2012 ident: 10.1016/j.ins.2015.07.025_bib0025 article-title: Improving risk predictions by preprocessing imbalanced credit data – start-page: 1 year: 2006 ident: 10.1016/j.ins.2015.07.025_bib0035 article-title: Measures of geometrical complexity in classification problems – year: 1993 ident: 10.1016/j.ins.2015.07.025_bib0053 – volume: 11 start-page: 131 year: 1999 ident: 10.1016/j.ins.2015.07.025_bib0008 article-title: Identifying mislabeled training data publication-title: J. Artif. Intell. Res. doi: 10.1613/jair.606 – start-page: 1 year: 2014 ident: 10.1016/j.ins.2015.07.025_bib0050 article-title: Class imbalance revisited: a new experimental setup to assess the performance of treatment methods – volume: 24 start-page: 136 issue: 1 year: 2012 ident: 10.1016/j.ins.2015.07.025_bib0015 article-title: Hellinger distance decision trees are robust and skew-insensitive publication-title: Data Min. Know. Discovery doi: 10.1007/s10618-011-0222-1 – volume: 16 start-page: 321 year: 2002 ident: 10.1016/j.ins.2015.07.025_bib0010 article-title: SMOTE: synthetic minority over-sampling technique publication-title: J. Artif. Intell. Res. doi: 10.1613/jair.953 – start-page: 97 year: 1999 ident: 10.1016/j.ins.2015.07.025_bib0020 article-title: AdaCost: misclassification cost-sensitive boosting – ident: 10.1016/j.ins.2015.07.025_bib0002 – volume: 24 start-page: 289 issue: 3 year: 2002 ident: 10.1016/j.ins.2015.07.025_bib0034 article-title: Complexity measures of supervised classification problems publication-title: IEEE Transactions on Pattern Anal. Mach. Intell. doi: 10.1109/34.990132 – volume: 45 start-page: 5 issue: 1 year: 2001 ident: 10.1016/j.ins.2015.07.025_bib0007 article-title: Random forests publication-title: Machine learn. doi: 10.1023/A:1010933404324 – start-page: 148 year: 1996 ident: 10.1016/j.ins.2015.07.025_bib0022 article-title: Experiments with a new boosting algorithm – volume: 10 start-page: 1895 issue: 7 year: 1998 ident: 10.1016/j.ins.2015.07.025_bib0018 article-title: Approximate statistical tests for comparing supervised classification learning algorithms publication-title: Neural comput. doi: 10.1162/089976698300017197 – volume: 38 start-page: 343 issue: 1 year: 2011 ident: 10.1016/j.ins.2015.07.025_bib0026 article-title: Constructing ensembles of classifiers using supervised projection methods based on misclassified instances publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2010.06.072 – ident: 10.1016/j.ins.2015.07.025_bib0062 – volume: 3 start-page: 131 issue: 2 year: 1999 ident: 10.1016/j.ins.2015.07.025_bib0052 article-title: A survey of methods for scaling up inductive algorithms publication-title: Data min. knowledge discovery doi: 10.1023/A:1009876119989 – volume: 62 start-page: 434 issue: 2 year: 2013 ident: 10.1016/j.ins.2015.07.025_bib0065 article-title: Using class imbalance learning for software defect prediction publication-title: IEEE Transactions on Reliability doi: 10.1109/TR.2013.2259203 – volume: 2 start-page: 408 issue: 3 year: 1972 ident: 10.1016/j.ins.2015.07.025_bib0069 article-title: Asymptotic properties of nearest neighbor rules using edited data publication-title: IEEE Transactions on Syst. Man Cybernetics doi: 10.1109/TSMC.1972.4309137 – volume: 28 start-page: 1619 issue: 10 year: 2006 ident: 10.1016/j.ins.2015.07.025_bib0055 article-title: Rotation forest: a new classifier ensemble method publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence doi: 10.1109/TPAMI.2006.211 – start-page: 277 year: 2013 ident: 10.1016/j.ins.2015.07.025_bib0059 article-title: Overlapping, rare examples and class decomposition in learning classifiers from imbalanced data – volume: 11 start-page: 10 issue: 1 year: 2009 ident: 10.1016/j.ins.2015.07.025_bib0030 article-title: The WEKA data mining software: an update publication-title: ACM SIGKDD Explor. Newslett. doi: 10.1145/1656274.1656278 – start-page: 324 year: 2009 ident: 10.1016/j.ins.2015.07.025_bib0064 article-title: Diversity analysis on imbalanced data sets by using ensemble models – volume: 85 start-page: 96 year: 2015 ident: 10.1016/j.ins.2015.07.025_bib0019 article-title: Random balance: Ensembles of variable priors classiffiers for imbalanced data publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2015.04.022 – start-page: 204 year: 2001 ident: 10.1016/j.ins.2015.07.025_bib0071 article-title: Learning and making decisions when costs and probabilities are both unknown – volume: 22 start-page: 1388 issue: 10 year: 2010 ident: 10.1016/j.ins.2015.07.025_bib0066 article-title: Combating the small sample class imbalance problem using feature selection publication-title: Knowledge Data Eng. IEEE Transactions on doi: 10.1109/TKDE.2009.187 – start-page: 878 year: 2005 ident: 10.1016/j.ins.2015.07.025_bib0031 article-title: Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning – year: 2004 ident: 10.1016/j.ins.2015.07.025_bib0040 – volume: 21 start-page: 1624 issue: 10 year: 2010 ident: 10.1016/j.ins.2015.07.025_bib0013 article-title: Ramoboost: ranked minority oversampling in boosting publication-title: Neural Net. IEEE Transactions on doi: 10.1109/TNN.2010.2066988 – year: 2009 ident: 10.1016/j.ins.2015.07.025_bib0054 – year: 2010 ident: 10.1016/j.ins.2015.07.025_bib0048 article-title: Documentation for the data complexity library in C++ |
| SSID | ssj0004766 |
| Score | 2.5367181 |
| Snippet | Many real-life problems can be described as unbalanced, where the number of instances belonging to one of the classes is much larger than the numbers in other... |
| SourceID | proquest crossref elsevier |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 98 |
| SubjectTerms | Classifier ensembles Classifiers Diversity Forests Fraud Imbalanced data sets Learning Oversampling Performance enhancement Repositories Rotation forest SMOTE Undersampling |
| Title | Diversity techniques improve the performance of the best imbalance learning ensembles |
| URI | https://dx.doi.org/10.1016/j.ins.2015.07.025 https://www.proquest.com/docview/1778045986 |
| Volume | 325 |
| WOSCitedRecordID | wos000362380600007&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: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1872-6291 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0004766 issn: 0020-0255 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3Lb9MwGLdKxwEOEwwQYwwZCe0AilTnneMEnWBU3YRaqTfLjm1o1SUlaaoJ8cfzOXYeDDGNA5eocp208vfL934g9IZwNwRY6OaDRDh-nCYO5yGYKmHApQg8LlxRD5uIptN4sUguB4OfTS3Mbh1lWXx9nWz-K6lhDYitS2f_gdztQ2EBPgPR4Qpkh-udCP-hzbRo-7OWuhiyyHfSFEb1agVshgAH2QB7uM5zhNV14y8BG1de8bXNM1w1We9txeM7K0C76fRL-cO5ZOXWeAF0fKHLHf6Si2L5tTI-6_NKF0616T96pBFzLsq8WBrnrSxZ0bl0P4P4_SZ3taY7qcTVcs3sl9ZjQQKd_eGO-lwYTFZty_S5sOcGPT5qJlNbiUxMdecfzN74HVZgoei-6ySou7Dax_zWWHt6Qc_mkwmdjRezk813R88c07F5O4DlHtpzoyCJh2jv9NN4cd7V1UYm1t383yYqXucH3vjVv-k1NyR8rbbMHqF9a2_gU4OTx2ggswP0sNeF8gAd29oVfIJ7pMWW6z9B8xZRuEMUtojCAB_cQxTOVb2kEYVbROEGUbhF1FM0PxvP3n907DQOJ_W80dYh0mWe0Oq-zwI3Db1UqhjEgwILIyCcx64SRCmPqJAoriKWiIgJ5QYpmOQsFd4zNMzyTD5H2BehlBEBXVQpX_KEwX4lUjcVkdT9Hg_RqDlLmtpW9Xpiypo2OYkrCsdP9fHTUUTh-A_R2_aWjenTcttmvyEQte-JUSApQOu22143xKTAhHVkjWUyr0pKIt3HS486eHGHPUfoQfdavETDbVHJY3Q_3W2XZfHKgvAXZq6uXw |
| 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=Diversity+techniques+improve+the+performance+of+the+best+imbalance+learning+ensembles&rft.jtitle=Information+sciences&rft.au=Diez-Pastor%2C+Jose+F&rft.au=Rodriguez%2C+Juan+J&rft.au=Garcia-Osorio%2C+Cesar+I&rft.au=Kuncheva%2C+Ludmila+I&rft.date=2015-12-20&rft.issn=0020-0255&rft.volume=325&rft.spage=98&rft.epage=117&rft_id=info:doi/10.1016%2Fj.ins.2015.07.025&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0020-0255&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0020-0255&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0020-0255&client=summon |