Class prediction for high-dimensional class-imbalanced data
Background The goal of class prediction studies is to develop rules to accurately predict the class membership of new samples. The rules are derived using the values of the variables available for each subject: the main characteristic of high-dimensional data is that the number of variables greatly...
Uloženo v:
| Vydáno v: | BMC bioinformatics Ročník 11; číslo 1; s. 523 |
|---|---|
| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
London
BioMed Central
20.10.2010
Springer Nature B.V BMC |
| Témata: | |
| ISSN: | 1471-2105, 1471-2105 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Background
The goal of class prediction studies is to develop rules to accurately predict the class membership of new samples. The rules are derived using the values of the variables available for each subject: the main characteristic of high-dimensional data is that the number of variables greatly exceeds the number of samples. Frequently the classifiers are developed using class-imbalanced data, i.e., data sets where the number of samples in each class is not equal. Standard classification methods used on class-imbalanced data often produce classifiers that do not accurately predict the minority class; the prediction is biased towards the majority class. In this paper we investigate if the high-dimensionality poses additional challenges when dealing with class-imbalanced prediction. We evaluate the performance of six types of classifiers on class-imbalanced data, using simulated data and a publicly available data set from a breast cancer gene-expression microarray study. We also investigate the effectiveness of some strategies that are available to overcome the effect of class imbalance.
Results
Our results show that the evaluated classifiers are highly sensitive to class imbalance and that variable selection introduces an additional bias towards classification into the majority class. Most new samples are assigned to the majority class from the training set, unless the difference between the classes is very large. As a consequence, the class-specific predictive accuracies differ considerably. When the class imbalance is not too severe, down-sizing and asymmetric bagging embedding variable selection work well, while over-sampling does not. Variable normalization can further worsen the performance of the classifiers.
Conclusions
Our results show that matching the prevalence of the classes in training and test set does not guarantee good performance of classifiers and that the problems related to classification with class-imbalanced data are exacerbated when dealing with high-dimensional data. Researchers using class-imbalanced data should be careful in assessing the predictive accuracy of the classifiers and, unless the class imbalance is mild, they should always use an appropriate method for dealing with the class imbalance problem. |
|---|---|
| AbstractList | Abstract Background: The goal of class prediction studies is to develop rules to accurately predict the class membership of new samples. The rules are derived using the values of the variables available for each subject: the main characteristic of high-dimensional data is that the number of variables greatly exceeds the number of samples. Frequently the classifiers are developed using class-imbalanced data, i.e., data sets where the number of samples in each class is not equal. Standard classification methods used on class-imbalanced data often produce classifiers that do not accurately predict the minority class; the prediction is biased towards the majority class. In this paper we investigate if the high-dimensionality poses additional challenges when dealing with class-imbalanced prediction. We evaluate the performance of six types of classifiers on class-imbalanced data, using simulated data and a publicly available data set from a breast cancer gene-expression microarray study. We also investigate the effectiveness of some strategies that are available to overcome the effect of class imbalance. Results: Our results show that the evaluated classifiers are highly sensitive to class imbalance and that variable selection introduces an additional bias towards classification into the majority class. Most new samples are assigned to the majority class from the training set, unless the difference between the classes is very large. As a consequence, the class-specific predictive accuracies differ considerably. When the class imbalance is not too severe, down-sizing and asymmetric bagging embedding variable selection work well, while over-sampling does not. Variable normalization can further worsen the performance of the classifiers. Conclusions: Our results show that matching the prevalence of the classes in training and test set does not guarantee good performance of classifiers and that the problems related to classification with class-imbalanced data are exacerbated when dealing with high-dimensional data. Researchers using class-imbalanced data should be careful in assessing the predictive accuracy of the classifiers and, unless the class imbalance is mild, they should always use an appropriate method for dealing with the class imbalance problem. The goal of class prediction studies is to develop rules to accurately predict the class membership of new samples. The rules are derived using the values of the variables available for each subject: the main characteristic of high-dimensional data is that the number of variables greatly exceeds the number of samples. Frequently the classifiers are developed using class-imbalanced data, i.e., data sets where the number of samples in each class is not equal. Standard classification methods used on class-imbalanced data often produce classifiers that do not accurately predict the minority class; the prediction is biased towards the majority class. In this paper we investigate if the high-dimensionality poses additional challenges when dealing with class-imbalanced prediction. We evaluate the performance of six types of classifiers on class-imbalanced data, using simulated data and a publicly available data set from a breast cancer gene-expression microarray study. We also investigate the effectiveness of some strategies that are available to overcome the effect of class imbalance.BACKGROUNDThe goal of class prediction studies is to develop rules to accurately predict the class membership of new samples. The rules are derived using the values of the variables available for each subject: the main characteristic of high-dimensional data is that the number of variables greatly exceeds the number of samples. Frequently the classifiers are developed using class-imbalanced data, i.e., data sets where the number of samples in each class is not equal. Standard classification methods used on class-imbalanced data often produce classifiers that do not accurately predict the minority class; the prediction is biased towards the majority class. In this paper we investigate if the high-dimensionality poses additional challenges when dealing with class-imbalanced prediction. We evaluate the performance of six types of classifiers on class-imbalanced data, using simulated data and a publicly available data set from a breast cancer gene-expression microarray study. We also investigate the effectiveness of some strategies that are available to overcome the effect of class imbalance.Our results show that the evaluated classifiers are highly sensitive to class imbalance and that variable selection introduces an additional bias towards classification into the majority class. Most new samples are assigned to the majority class from the training set, unless the difference between the classes is very large. As a consequence, the class-specific predictive accuracies differ considerably. When the class imbalance is not too severe, down-sizing and asymmetric bagging embedding variable selection work well, while over-sampling does not. Variable normalization can further worsen the performance of the classifiers.RESULTSOur results show that the evaluated classifiers are highly sensitive to class imbalance and that variable selection introduces an additional bias towards classification into the majority class. Most new samples are assigned to the majority class from the training set, unless the difference between the classes is very large. As a consequence, the class-specific predictive accuracies differ considerably. When the class imbalance is not too severe, down-sizing and asymmetric bagging embedding variable selection work well, while over-sampling does not. Variable normalization can further worsen the performance of the classifiers.Our results show that matching the prevalence of the classes in training and test set does not guarantee good performance of classifiers and that the problems related to classification with class-imbalanced data are exacerbated when dealing with high-dimensional data. Researchers using class-imbalanced data should be careful in assessing the predictive accuracy of the classifiers and, unless the class imbalance is mild, they should always use an appropriate method for dealing with the class imbalance problem.CONCLUSIONSOur results show that matching the prevalence of the classes in training and test set does not guarantee good performance of classifiers and that the problems related to classification with class-imbalanced data are exacerbated when dealing with high-dimensional data. Researchers using class-imbalanced data should be careful in assessing the predictive accuracy of the classifiers and, unless the class imbalance is mild, they should always use an appropriate method for dealing with the class imbalance problem. The goal of class prediction studies is to develop rules to accurately predict the class membership of new samples. The rules are derived using the values of the variables available for each subject: the main characteristic of high-dimensional data is that the number of variables greatly exceeds the number of samples. Frequently the classifiers are developed using class-imbalanced data, i.e., data sets where the number of samples in each class is not equal. Standard classification methods used on class-imbalanced data often produce classifiers that do not accurately predict the minority class; the prediction is biased towards the majority class. In this paper we investigate if the high-dimensionality poses additional challenges when dealing with class-imbalanced prediction. We evaluate the performance of six types of classifiers on class-imbalanced data, using simulated data and a publicly available data set from a breast cancer gene-expression microarray study. We also investigate the effectiveness of some strategies that are available to overcome the effect of class imbalance. Our results show that the evaluated classifiers are highly sensitive to class imbalance and that variable selection introduces an additional bias towards classification into the majority class. Most new samples are assigned to the majority class from the training set, unless the difference between the classes is very large. As a consequence, the class-specific predictive accuracies differ considerably. When the class imbalance is not too severe, down-sizing and asymmetric bagging embedding variable selection work well, while over-sampling does not. Variable normalization can further worsen the performance of the classifiers. Our results show that matching the prevalence of the classes in training and test set does not guarantee good performance of classifiers and that the problems related to classification with class-imbalanced data are exacerbated when dealing with high-dimensional data. Researchers using class-imbalanced data should be careful in assessing the predictive accuracy of the classifiers and, unless the class imbalance is mild, they should always use an appropriate method for dealing with the class imbalance problem. Background The goal of class prediction studies is to develop rules to accurately predict the class membership of new samples. The rules are derived using the values of the variables available for each subject: the main characteristic of high-dimensional data is that the number of variables greatly exceeds the number of samples. Frequently the classifiers are developed using class-imbalanced data, i.e., data sets where the number of samples in each class is not equal. Standard classification methods used on class-imbalanced data often produce classifiers that do not accurately predict the minority class; the prediction is biased towards the majority class. In this paper we investigate if the high-dimensionality poses additional challenges when dealing with class-imbalanced prediction. We evaluate the performance of six types of classifiers on class-imbalanced data, using simulated data and a publicly available data set from a breast cancer gene-expression microarray study. We also investigate the effectiveness of some strategies that are available to overcome the effect of class imbalance. Results Our results show that the evaluated classifiers are highly sensitive to class imbalance and that variable selection introduces an additional bias towards classification into the majority class. Most new samples are assigned to the majority class from the training set, unless the difference between the classes is very large. As a consequence, the class-specific predictive accuracies differ considerably. When the class imbalance is not too severe, down-sizing and asymmetric bagging embedding variable selection work well, while over-sampling does not. Variable normalization can further worsen the performance of the classifiers. Conclusions Our results show that matching the prevalence of the classes in training and test set does not guarantee good performance of classifiers and that the problems related to classification with class-imbalanced data are exacerbated when dealing with high-dimensional data. Researchers using class-imbalanced data should be careful in assessing the predictive accuracy of the classifiers and, unless the class imbalance is mild, they should always use an appropriate method for dealing with the class imbalance problem. |
| ArticleNumber | 523 |
| Author | Blagus, Rok Lusa, Lara |
| AuthorAffiliation | 1 Institute for Biostatistics and Medical Informatics, University of Ljubljana, Vrazov trg 2, Ljubljana, Slovenia |
| AuthorAffiliation_xml | – name: 1 Institute for Biostatistics and Medical Informatics, University of Ljubljana, Vrazov trg 2, Ljubljana, Slovenia |
| Author_xml | – sequence: 1 givenname: Rok surname: Blagus fullname: Blagus, Rok organization: Institute for Biostatistics and Medical Informatics, University of Ljubljana – sequence: 2 givenname: Lara surname: Lusa fullname: Lusa, Lara email: lara.lusa@mf.uni-lj.si organization: Institute for Biostatistics and Medical Informatics, University of Ljubljana |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/20961420$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kktr3DAUhUVJaB7tvqtiuunKjSRLlkyhUIY-AoFu2rW4enhGg21NJU-g_77XnSRNAslK0tV3Dlf36IwcTWkKhLxh9ANjur1gQrGaMyprxmrJmxfk9K50dG9_Qs5K2VLKlKbyJTnhtGuZ4PSUfFwNUEq1y8FHN8c0VX3K1SauN7WPY5gKlmCo3ELVcbQwwOSCrzzM8Ioc9zCU8PpmPSe_vn75ufpeX_34drn6fFU72bVzzYXUohUOmGu90yooYVttrWKy6ZvQCEu5Z33ve9fLRlvtBLW8FSCsk0La5pxcHnx9gq3Z5ThC_mMSRPOvkPLaQJ6jG4JhvreaSQWoF1LZjnInLFigjeg44-j16eC129sxeBemOcPwwPThzRQ3Zp2uTUM7TbVCg_c3Bjn93ocymzEWFwacS0j7YhDC9ypKkXz3iNymfcZpFtNRTK_DPBB6e7-fu0ZuE0KgPQAup1Jy6I2LMyxJYXtxMIya5SuYJWuzZI1Hg18BhfSR8Nb7GQk7SAqi0zrk_x0_qfkL0DXC2g |
| CitedBy_id | crossref_primary_10_1186_s13321_020_00420_z crossref_primary_10_1016_j_inffus_2024_102874 crossref_primary_10_1016_j_chemolab_2019_103906 crossref_primary_10_1016_j_eswa_2017_06_026 crossref_primary_10_1038_s41598_024_61334_6 crossref_primary_10_1186_1471_2105_14_119 crossref_primary_10_1016_j_compmedimag_2013_12_003 crossref_primary_10_2217_bmm_15_20 crossref_primary_10_3390_info12070266 crossref_primary_10_1007_s42979_020_0085_x crossref_primary_10_2196_28856 crossref_primary_10_3390_app10238324 crossref_primary_10_3758_s13428_023_02301_3 crossref_primary_10_1016_j_polymdegradstab_2013_12_035 crossref_primary_10_1016_j_rcsop_2023_100307 crossref_primary_10_1186_s12859_020_3411_3 crossref_primary_10_1016_j_eswa_2019_06_014 crossref_primary_10_1186_1471_2105_15_S5_S4 crossref_primary_10_1109_TCYB_2013_2257480 crossref_primary_10_1007_s11548_013_0913_8 crossref_primary_10_3390_metabo10070299 crossref_primary_10_3390_s23187946 crossref_primary_10_1007_s11548_020_02260_6 crossref_primary_10_1007_s12553_019_00375_8 crossref_primary_10_3390_genes11070717 crossref_primary_10_1016_j_patcog_2019_01_047 crossref_primary_10_1007_s00500_014_1484_5 crossref_primary_10_1093_nar_gkaa1161 crossref_primary_10_1049_iet_sen_2018_5193 crossref_primary_10_1016_j_chemolab_2017_11_009 crossref_primary_10_1016_j_rse_2011_05_013 crossref_primary_10_1080_01431161_2018_1433343 crossref_primary_10_3390_rs11121409 crossref_primary_10_1016_j_knosys_2014_01_021 crossref_primary_10_1016_j_asoc_2018_02_051 crossref_primary_10_1088_2057_1976_ad9157 crossref_primary_10_1080_14767058_2023_2254891 crossref_primary_10_1111_1755_0998_12773 crossref_primary_10_1061__ASCE_NH_1527_6996_0000493 crossref_primary_10_3390_metabo13020301 crossref_primary_10_1007_s10791_025_09576_4 crossref_primary_10_1016_j_engstruct_2022_114126 crossref_primary_10_1016_j_ins_2016_09_038 crossref_primary_10_1371_journal_pone_0280399 crossref_primary_10_1152_jn_00521_2017 crossref_primary_10_1007_s12665_021_09536_3 crossref_primary_10_1186_1471_2164_13_630 crossref_primary_10_3390_rs14153666 crossref_primary_10_1016_j_compbiolchem_2019_03_017 crossref_primary_10_1016_j_trc_2018_09_020 crossref_primary_10_1007_s11693_015_9162_1 crossref_primary_10_1016_j_knosys_2015_05_027 crossref_primary_10_1080_2150704X_2017_1362124 crossref_primary_10_1016_j_eswa_2021_116437 crossref_primary_10_1080_14740338_2017_1325463 crossref_primary_10_1155_2013_239628 crossref_primary_10_1016_j_asoc_2018_12_024 crossref_primary_10_20965_jdr_2017_p0646 crossref_primary_10_1080_21681015_2016_1268216 crossref_primary_10_1016_j_ipm_2024_103972 crossref_primary_10_1186_s12874_021_01374_y crossref_primary_10_3389_fphar_2025_1546878 crossref_primary_10_1016_j_ecolind_2019_106037 crossref_primary_10_1016_j_tplants_2014_08_004 crossref_primary_10_1038_s41540_019_0099_y crossref_primary_10_1016_j_engappai_2022_104828 crossref_primary_10_1088_1742_6596_1878_1_012054 crossref_primary_10_1042_BSR20203859 crossref_primary_10_1002_ima_22615 crossref_primary_10_1109_TCBB_2014_2306838 crossref_primary_10_1111_ppa_13988 crossref_primary_10_1186_1471_2164_13_170 crossref_primary_10_1109_TCE_2021_3129316 crossref_primary_10_1016_j_csda_2016_07_016 crossref_primary_10_1038_s41598_021_95128_x crossref_primary_10_1186_s12859_017_1602_3 crossref_primary_10_3390_diagnostics12123138 crossref_primary_10_1186_s12859_015_0723_9 crossref_primary_10_1093_bib_bbs026 crossref_primary_10_1016_j_neucom_2011_03_054 crossref_primary_10_1088_1741_2552_ab9986 crossref_primary_10_1093_bib_bbw107 crossref_primary_10_1016_j_neucom_2012_11_056 crossref_primary_10_7717_peerj_cs_832 crossref_primary_10_1080_03610918_2022_2049820 crossref_primary_10_1186_s12859_024_05677_x crossref_primary_10_1007_s12038_019_9909_z crossref_primary_10_1007_s11764_023_01465_3 crossref_primary_10_1016_j_eswa_2016_12_035 crossref_primary_10_1016_j_neucom_2016_05_111 crossref_primary_10_2147_RMHP_S442138 crossref_primary_10_1007_s12652_021_03256_z crossref_primary_10_1155_2014_957107 crossref_primary_10_1186_s12911_017_0522_5 crossref_primary_10_1016_j_xops_2024_100699 crossref_primary_10_1038_s41398_022_01938_6 crossref_primary_10_1016_j_euroneuro_2024_08_005 crossref_primary_10_1007_s13748_017_0136_2 crossref_primary_10_1093_jat_bkab098 crossref_primary_10_1186_1471_2105_14_106 crossref_primary_10_1016_j_geomorph_2014_04_006 crossref_primary_10_1007_s10844_017_0446_7 crossref_primary_10_1016_j_ecoinf_2019_01_008 crossref_primary_10_3390_info12080286 crossref_primary_10_1007_s42979_020_00210_2 crossref_primary_10_1002_widm_1072 crossref_primary_10_1093_bib_bbs006 crossref_primary_10_3233_JIFS_190821 crossref_primary_10_1007_s00521_024_09713_y crossref_primary_10_1016_j_ins_2019_07_070 crossref_primary_10_1016_j_compbiomed_2021_104644 crossref_primary_10_1093_bfgp_elad031 crossref_primary_10_1371_journal_pone_0021681 crossref_primary_10_3390_rs15051208 crossref_primary_10_1016_j_ophtha_2021_01_019 crossref_primary_10_3390_rs8020161 crossref_primary_10_1109_TGRS_2025_3574343 crossref_primary_10_1186_1758_2946_4_16 crossref_primary_10_3390_cancers15010140 crossref_primary_10_1186_s12859_017_1578_z crossref_primary_10_1186_s12864_019_6413_7 crossref_primary_10_1038_s41467_024_45198_y crossref_primary_10_1016_j_ecoinf_2023_102155 crossref_primary_10_32604_cmc_2023_036025 crossref_primary_10_1080_00071668_2017_1379051 crossref_primary_10_1109_ACCESS_2021_3105000 crossref_primary_10_1186_1752_0509_9_S5_S1 crossref_primary_10_1186_1471_2105_14_64 |
| Cites_doi | 10.1109/TSMCB.2008.2007853 10.1056/NEJMe068292 10.1198/016214502753479248 10.1038/ng1307 10.1023/A:1010933404324 10.1186/1471-2105-9-S6-S7 10.1093/biostatistics/kxg046 10.1016/j.csda.2006.12.043 10.2174/1386207013330733 10.1016/S0140-6736(03)12775-4 10.1038/4462 10.1186/1471-2105-7-228 10.1145/1007730.1007735 10.1109/TPAMI.2006.134 10.1200/JCO.2006.06.1879 10.1109/TKDE.2008.239 10.1038/ng1060 10.1093/bioinformatics/16.10.906 10.1093/bioinformatics/btm344 10.1093/jnci/djm216 10.1038/ng1547 10.1093/oso/9780198509844.001.0001 10.1201/9780203011232 10.1016/S0140-6736(05)17866-0 10.1073/pnas.1732912100 10.1093/bioinformatics/btn583 10.1073/pnas.082099299 10.1186/1471-2105-6-68 10.1038/nature03702 10.2165/00822942-200504030-00004 10.1073/pnas.97.1.262 10.1038/nm0102-68 10.1007/978-1-4757-3462-1 10.3233/IDA-2002-6504 |
| ContentType | Journal Article |
| Copyright | Blagus and Lusa; licensee BioMed Central Ltd. 2010 2010 Blagus and Lusa; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Copyright ©2010 Blagus and Lusa; licensee BioMed Central Ltd. 2010 Blagus and Lusa; licensee BioMed Central Ltd. |
| Copyright_xml | – notice: Blagus and Lusa; licensee BioMed Central Ltd. 2010 – notice: 2010 Blagus and Lusa; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. – notice: Copyright ©2010 Blagus and Lusa; licensee BioMed Central Ltd. 2010 Blagus and Lusa; licensee BioMed Central Ltd. |
| DBID | C6C AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7QO 7SC 7X7 7XB 88E 8AL 8AO 8FD 8FE 8FG 8FH 8FI 8FJ 8FK ABUWG AEUYN AFKRA ARAPS AZQEC BBNVY BENPR BGLVJ BHPHI CCPQU DWQXO FR3 FYUFA GHDGH GNUQQ HCIFZ JQ2 K7- K9. L7M LK8 L~C L~D M0N M0S M1P M7P P5Z P62 P64 PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS Q9U 7X8 5PM DOA |
| DOI | 10.1186/1471-2105-11-523 |
| DatabaseName | Springer Nature OA Free Journals CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Biotechnology Research Abstracts Computer and Information Systems Abstracts Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Computing Database (Alumni Edition) ProQuest Pharma Collection Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Collection ProQuest Hospital Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest One Sustainability ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials - QC Biological Science Collection ProQuest Central Technology Collection Natural Science Collection ProQuest One Community College ProQuest Central Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Computer Science Collection Computer Science Database ProQuest Health & Medical Complete (Alumni) Advanced Technologies Database with Aerospace Biological Sciences Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Computing Database ProQuest Health & Medical Collection Medical Database Biological Science Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts Proquest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China ProQuest Central Basic MEDLINE - Academic PubMed Central (Full Participant titles) Directory of Open Access Journals (DOAJ) |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database Computer Science Database ProQuest Central Student ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts SciTech Premium Collection ProQuest Central China ProQuest One Applied & Life Sciences ProQuest One Sustainability Health Research Premium Collection Natural Science Collection Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) Advanced Technologies & Aerospace Collection ProQuest Biological Science Collection ProQuest One Academic Eastern Edition ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts ProQuest Health & Medical Complete ProQuest One Academic UKI Edition Engineering Research Database ProQuest One Academic ProQuest One Academic (New) Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Pharma Collection ProQuest Central ProQuest Health & Medical Research Collection Biotechnology Research Abstracts Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Advanced Technologies Database with Aerospace ProQuest Computing ProQuest Central Basic ProQuest Computing (Alumni Edition) ProQuest SciTech Collection Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database ProQuest Medical Library ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | Publicly Available Content Database MEDLINE - Academic MEDLINE |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology |
| EISSN | 1471-2105 |
| EndPage | 523 |
| ExternalDocumentID | oai_doaj_org_article_1dfb8157a264457b902c4baba0349212 PMC3098087 2501710671 20961420 10_1186_1471_2105_11_523 |
| Genre | Journal Article |
| GroupedDBID | --- 0R~ 23N 2VQ 2WC 4.4 53G 5VS 6J9 7X7 88E 8AO 8FE 8FG 8FH 8FI 8FJ AAFWJ AAJSJ AAKPC AASML ABDBF ABUWG ACGFO ACGFS ACIHN ACIWK ACPRK ACUHS ADBBV ADMLS ADRAZ ADUKV AEAQA AENEX AEUYN AFKRA AFPKN AFRAH AHBYD AHMBA AHSBF AHYZX ALMA_UNASSIGNED_HOLDINGS AMKLP AMTXH AOIJS ARAPS AZQEC BAPOH BAWUL BBNVY BCNDV BENPR BFQNJ BGLVJ BHPHI BMC BPHCQ BVXVI C1A C6C CCPQU CS3 DIK DU5 DWQXO E3Z EAD EAP EAS EBD EBLON EBS EJD EMB EMK EMOBN ESX F5P FYUFA GNUQQ GROUPED_DOAJ GX1 H13 HCIFZ HMCUK HYE IAO ICD IHR INH INR IPNFZ ISR ITC K6V K7- KQ8 LK8 M1P M48 M7P MK~ ML0 M~E O5R O5S OK1 OVT P2P P62 PGMZT PHGZM PHGZT PIMPY PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO PUEGO RBZ RIG RNS ROL RPM RSV SBL SOJ SV3 TR2 TUS UKHRP W2D WOQ WOW XH6 XSB AAYXX AFFHD CITATION -A0 3V. ACRMQ ADINQ ALIPV C24 CGR CUY CVF ECM EIF M0N NPM 7QO 7SC 7XB 8AL 8FD 8FK FR3 JQ2 K9. L7M L~C L~D P64 PKEHL PQEST PQUKI PRINS Q9U 7X8 5PM |
| ID | FETCH-LOGICAL-c596t-2458464ca1c6dc87e74b68bb7153f3e34b02d1ffdfcf538b8c40b264a4bc545b3 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 181 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000284532400002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1471-2105 |
| IngestDate | Mon Nov 10 04:25:54 EST 2025 Tue Nov 04 02:02:33 EST 2025 Mon Nov 24 07:41:54 EST 2025 Mon Oct 06 18:17:52 EDT 2025 Wed Feb 19 01:47:46 EST 2025 Sat Nov 29 05:39:50 EST 2025 Tue Nov 18 22:25:26 EST 2025 Sat Sep 06 07:27:15 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Classification Rule Variable Selection Predictive Accuracy Minority Class Class Imbalance |
| Language | English |
| License | http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c596t-2458464ca1c6dc87e74b68bb7153f3e34b02d1ffdfcf538b8c40b264a4bc545b3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| OpenAccessLink | https://doaj.org/article/1dfb8157a264457b902c4baba0349212 |
| PMID | 20961420 |
| PQID | 901869001 |
| PQPubID | 44065 |
| PageCount | 1 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_1dfb8157a264457b902c4baba0349212 pubmedcentral_primary_oai_pubmedcentral_nih_gov_3098087 proquest_miscellaneous_808458700 proquest_journals_901869001 pubmed_primary_20961420 crossref_citationtrail_10_1186_1471_2105_11_523 crossref_primary_10_1186_1471_2105_11_523 springer_journals_10_1186_1471_2105_11_523 |
| PublicationCentury | 2000 |
| PublicationDate | 2010-10-20 |
| PublicationDateYYYYMMDD | 2010-10-20 |
| PublicationDate_xml | – month: 10 year: 2010 text: 2010-10-20 day: 20 |
| PublicationDecade | 2010 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England |
| PublicationTitle | BMC bioinformatics |
| PublicationTitleAbbrev | BMC Bioinformatics |
| PublicationTitleAlternate | BMC Bioinformatics |
| PublicationYear | 2010 |
| Publisher | BioMed Central Springer Nature B.V BMC |
| Publisher_xml | – name: BioMed Central – name: Springer Nature B.V – name: BMC |
| References | KL Gunderson (4106_CR4) 2005; 37 J Massague (4106_CR5) 2007; 356 L Breiman (4106_CR39) 2001; 45 D Tao (4106_CR24) 2006; 28 S Michiels (4106_CR43) 2005; 365 TP Speed (4106_CR11) 2003 C Sotiriou (4106_CR28) 2003; 100 N Japkowicz (4106_CR18) 2002; 6 HH Meng (4106_CR23) 2008 N Japkowicz (4106_CR31) 2000 E Fix Jr (4106_CR38) 1951 K Yang (4106_CR21) 2006; 7 MS Pepe (4106_CR29) 2003 L Lusa (4106_CR30) 2007; 99 MA Shipp (4106_CR16) 2002; 8 GEAPA Batista (4106_CR45) 2004; 6 J Wu (4106_CR9) 2009; 25 Y Saeys (4106_CR13) 2007; 23 H He (4106_CR19) 2009; 21 J Zhu (4106_CR42) 2004; 5 XY Liu (4106_CR32) 2009; 39 AS Ishkanian (4106_CR3) 2004; 36 R Tibshirani (4106_CR41) 2002; 99 S Ramaswamy (4106_CR15) 2003; 33 C Cortes (4106_CR40) 1995; 20 RM Simon (4106_CR12) 2004 S Dudoit (4106_CR14) 2002; 97 TS Furey (4106_CR20) 2000; 16 H Ahn (4106_CR25) 2007; 51 A Oberthuer (4106_CR7) 2006; 24 A Al-Shahib (4106_CR26) 2005; 4 Y Freund (4106_CR34) 1996 NV Chawla (4106_CR44) 2002; 16 R Development Core Team (4106_CR37) 2008 J Lu (4106_CR2) 2005; 435 L Li (4106_CR6) 2001; 4 T Hastie (4106_CR35) 2003 P Brown (4106_CR1) 1999; 21 MP Brown (4106_CR10) 2000; 97 N Iizuka (4106_CR17) 2003; 361 GZ Li (4106_CR27) 2008; 9 Y Freund (4106_CR33) 1995 I Levner (4106_CR22) 2005; 6 F Harrell (4106_CR36) 2001 PJ Tan (4106_CR8) 2007 16792098 - IEEE Trans Pattern Anal Mach Intell. 2006 Jul;28(7):1088-99 14981516 - Nat Genet. 2004 Mar;36(3):299-303 11120680 - Bioinformatics. 2000 Oct;16(10):906-14 15208204 - Biostatistics. 2004 Jul;5(3):427-43 19095540 - IEEE Trans Syst Man Cybern B Cybern. 2009 Apr;39(2):539-50 12917485 - Proc Natl Acad Sci U S A. 2003 Sep 2;100(18):10393-8 16231961 - Appl Bioinformatics. 2005;4(3):195-203 18000217 - J Natl Cancer Inst. 2007 Nov 21;99(22):1715-23 12469122 - Nat Genet. 2003 Jan;33(1):49-54 11894805 - Comb Chem High Throughput Screen. 2001 Dec;4(8):727-39 12011421 - Proc Natl Acad Sci U S A. 2002 May 14;99(10):6567-72 16643657 - BMC Bioinformatics. 2006;7:228 15944708 - Nature. 2005 Jun 9;435(7043):834-8 17075126 - J Clin Oncol. 2006 Nov 1;24(31):5070-8 15705458 - Lancet. 2005 Feb 5-11;365(9458):488-92 12648972 - Lancet. 2003 Mar 15;361(9361):923-9 17720704 - Bioinformatics. 2007 Oct 1;23(19):2507-17 15838508 - Nat Genet. 2005 May;37(5):549-54 11786909 - Nat Med. 2002 Jan;8(1):68-74 9915498 - Nat Genet. 1999 Jan;21(1 Suppl):33-7 15788095 - BMC Bioinformatics. 2005;6:68 19008251 - Bioinformatics. 2009 Jan 1;25(1):30-5 17229957 - N Engl J Med. 2007 Jan 18;356(3):294-7 18541060 - BMC Bioinformatics. 2008;9 Suppl 6:S7 10618406 - Proc Natl Acad Sci U S A. 2000 Jan 4;97(1):262-7 |
| References_xml | – volume: 39 start-page: 539 issue: 2 year: 2009 ident: 4106_CR32 publication-title: Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on doi: 10.1109/TSMCB.2008.2007853 – volume: 356 start-page: 294 issue: 3 year: 2007 ident: 4106_CR5 publication-title: N Engl J Med doi: 10.1056/NEJMe068292 – volume-title: Design and Analysis of DNA Microarray Investigations year: 2004 ident: 4106_CR12 – volume: 97 start-page: 77 issue: 457 year: 2002 ident: 4106_CR14 publication-title: J Amer Statistical Assoc doi: 10.1198/016214502753479248 – volume: 36 start-page: 299 issue: 3 year: 2004 ident: 4106_CR3 publication-title: Nat Genet doi: 10.1038/ng1307 – volume: 45 start-page: 5 year: 2001 ident: 4106_CR39 publication-title: Mach Learn doi: 10.1023/A:1010933404324 – volume: 9 start-page: S7 issue: Suppl 6 year: 2008 ident: 4106_CR27 publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-9-S6-S7 – volume: 5 start-page: 427 issue: 3 year: 2004 ident: 4106_CR42 publication-title: Biostatistics doi: 10.1093/biostatistics/kxg046 – volume: 51 start-page: 6166 issue: 12 year: 2007 ident: 4106_CR25 publication-title: Comput Stat Data Anal doi: 10.1016/j.csda.2006.12.043 – volume: 4 start-page: 727 year: 2001 ident: 4106_CR6 publication-title: Comb Chem High Throughput Screen doi: 10.2174/1386207013330733 – volume: 361 start-page: 923 issue: 9361 year: 2003 ident: 4106_CR17 publication-title: The Lancet doi: 10.1016/S0140-6736(03)12775-4 – volume: 21 start-page: 33 issue: Suppl 1 year: 1999 ident: 4106_CR1 publication-title: Nat Genet doi: 10.1038/4462 – start-page: 23 volume-title: A decision-theoretic generalization of on-line learning and an application to boosting year: 1995 ident: 4106_CR33 – start-page: 136 volume-title: LNOR 9: The Second International Symposium on Optimization and Systems Biology (OSB'08) year: 2008 ident: 4106_CR23 – volume: 7 start-page: 228 year: 2006 ident: 4106_CR21 publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-7-228 – start-page: 589 volume-title: Proceedings of the 20th Australian joint conference on Advances in artificial intelligence Volume 4830 of Lecture Notes in Computer Science year: 2007 ident: 4106_CR8 – volume: 6 start-page: 20 year: 2004 ident: 4106_CR45 publication-title: ACM SIGKDD Explor Newsl doi: 10.1145/1007730.1007735 – volume: 28 start-page: 1088 issue: 7 year: 2006 ident: 4106_CR24 publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2006.134 – volume: 24 start-page: 5070 issue: 31 year: 2006 ident: 4106_CR7 publication-title: J Clin Oncol doi: 10.1200/JCO.2006.06.1879 – volume: 21 start-page: 1263 issue: 9 year: 2009 ident: 4106_CR19 publication-title: IEEE Trans Knowledge and Data Eng doi: 10.1109/TKDE.2008.239 – volume: 33 start-page: 49 year: 2003 ident: 4106_CR15 publication-title: Nat Genet doi: 10.1038/ng1060 – volume: 16 start-page: 906 issue: 10 year: 2000 ident: 4106_CR20 publication-title: Bioinformatics doi: 10.1093/bioinformatics/16.10.906 – volume: 23 start-page: 2507 issue: 19 year: 2007 ident: 4106_CR13 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btm344 – volume: 99 start-page: 1715 issue: 22 year: 2007 ident: 4106_CR30 publication-title: J Natl Cancer Inst doi: 10.1093/jnci/djm216 – start-page: 111 volume-title: Proceedings of the 2000 International Conference on Artificial Intelligence (ICAI) year: 2000 ident: 4106_CR31 – volume: 37 start-page: 549 issue: 5 year: 2005 ident: 4106_CR4 publication-title: Nat Genet doi: 10.1038/ng1547 – volume-title: The Statistical Evaluation of Medical Tests for Classification and Prediction year: 2003 ident: 4106_CR29 doi: 10.1093/oso/9780198509844.001.0001 – volume-title: Statistical Analysis of Gene Expression Microarray Data year: 2003 ident: 4106_CR11 doi: 10.1201/9780203011232 – volume: 365 start-page: 488 issue: 9458 year: 2005 ident: 4106_CR43 publication-title: Lancet doi: 10.1016/S0140-6736(05)17866-0 – volume: 16 start-page: 341 issue: 2002 year: 2002 ident: 4106_CR44 publication-title: J Artif Intell Res – volume: 100 start-page: 10393 issue: 18 year: 2003 ident: 4106_CR28 publication-title: Proc Natl Acad Sci USA doi: 10.1073/pnas.1732912100 – volume: 25 start-page: 30 year: 2009 ident: 4106_CR9 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btn583 – volume: 99 start-page: 6567 issue: 10 year: 2002 ident: 4106_CR41 publication-title: Proc Natl Acad Sci USA doi: 10.1073/pnas.082099299 – start-page: 148 volume-title: Proceedings of the Thirteenth International Conference on Machine Learning year: 1996 ident: 4106_CR34 – volume: 6 start-page: 68 year: 2005 ident: 4106_CR22 publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-6-68 – volume: 435 start-page: 834 issue: 7043 year: 2005 ident: 4106_CR2 publication-title: Nature doi: 10.1038/nature03702 – volume: 4 start-page: 195 issue: 3 year: 2005 ident: 4106_CR26 publication-title: Appl Bioinformatics doi: 10.2165/00822942-200504030-00004 – volume-title: R: A language and environment for statistical computing year: 2008 ident: 4106_CR37 – volume: 20 start-page: 273 issue: 3 year: 1995 ident: 4106_CR40 publication-title: Mach Learn – volume-title: The Elements of Statistical Learning. Data Mining, Inference, and Prediction year: 2003 ident: 4106_CR35 – volume: 97 start-page: 262 year: 2000 ident: 4106_CR10 publication-title: Proc Natl Acad Sci doi: 10.1073/pnas.97.1.262 – volume: 8 start-page: 68 year: 2002 ident: 4106_CR16 publication-title: Nat Med doi: 10.1038/nm0102-68 – volume-title: Regression Modeling Strategies year: 2001 ident: 4106_CR36 doi: 10.1007/978-1-4757-3462-1 – volume-title: Tech. Rep. Project 21-49-004, Report Number 4, USAF School of Aviation Medicine, Randolf Field, Texas year: 1951 ident: 4106_CR38 – volume: 6 start-page: 429 issue: 5 year: 2002 ident: 4106_CR18 publication-title: Intell Data Anal doi: 10.3233/IDA-2002-6504 – reference: 16643657 - BMC Bioinformatics. 2006;7:228 – reference: 12648972 - Lancet. 2003 Mar 15;361(9361):923-9 – reference: 12917485 - Proc Natl Acad Sci U S A. 2003 Sep 2;100(18):10393-8 – reference: 17229957 - N Engl J Med. 2007 Jan 18;356(3):294-7 – reference: 19008251 - Bioinformatics. 2009 Jan 1;25(1):30-5 – reference: 18000217 - J Natl Cancer Inst. 2007 Nov 21;99(22):1715-23 – reference: 19095540 - IEEE Trans Syst Man Cybern B Cybern. 2009 Apr;39(2):539-50 – reference: 14981516 - Nat Genet. 2004 Mar;36(3):299-303 – reference: 11786909 - Nat Med. 2002 Jan;8(1):68-74 – reference: 17075126 - J Clin Oncol. 2006 Nov 1;24(31):5070-8 – reference: 15208204 - Biostatistics. 2004 Jul;5(3):427-43 – reference: 15944708 - Nature. 2005 Jun 9;435(7043):834-8 – reference: 11894805 - Comb Chem High Throughput Screen. 2001 Dec;4(8):727-39 – reference: 9915498 - Nat Genet. 1999 Jan;21(1 Suppl):33-7 – reference: 17720704 - Bioinformatics. 2007 Oct 1;23(19):2507-17 – reference: 16792098 - IEEE Trans Pattern Anal Mach Intell. 2006 Jul;28(7):1088-99 – reference: 10618406 - Proc Natl Acad Sci U S A. 2000 Jan 4;97(1):262-7 – reference: 15838508 - Nat Genet. 2005 May;37(5):549-54 – reference: 12011421 - Proc Natl Acad Sci U S A. 2002 May 14;99(10):6567-72 – reference: 15788095 - BMC Bioinformatics. 2005;6:68 – reference: 15705458 - Lancet. 2005 Feb 5-11;365(9458):488-92 – reference: 16231961 - Appl Bioinformatics. 2005;4(3):195-203 – reference: 12469122 - Nat Genet. 2003 Jan;33(1):49-54 – reference: 11120680 - Bioinformatics. 2000 Oct;16(10):906-14 – reference: 18541060 - BMC Bioinformatics. 2008;9 Suppl 6:S7 |
| SSID | ssj0017805 |
| Score | 2.4414792 |
| Snippet | Background
The goal of class prediction studies is to develop rules to accurately predict the class membership of new samples. The rules are derived using the... The goal of class prediction studies is to develop rules to accurately predict the class membership of new samples. The rules are derived using the values of... Abstract Background: The goal of class prediction studies is to develop rules to accurately predict the class membership of new samples. The rules are derived... Abstract Background The goal of class prediction studies is to develop rules to accurately predict the class membership of new samples. The rules are derived... |
| SourceID | doaj pubmedcentral proquest pubmed crossref springer |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 523 |
| SubjectTerms | Algorithms Bioinformatics Biomedical and Life Sciences Breast cancer Breast Neoplasms - classification Breast Neoplasms - genetics Classification Classification - methods Computational Biology/Bioinformatics Computer Appl. in Life Sciences Databases, Factual Discriminant analysis Female Gene Expression Profiling - methods Humans Life Sciences Microarrays Oligonucleotide Array Sequence Analysis - methods Research Article Studies Transcriptome analysis |
| SummonAdditionalLinks | – databaseName: Biological Science Database dbid: M7P link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwEB5BAYkLlHcoRTn0ApK1juPYjjhUgKh6qKoeoOrN8hNWguyyu0Xi3-NxHtXy6AUpl8ROZHs843Fm_H0AB5611NdBkRiMIBzP5RhpOPFSWEeFsyqn_J-fyNNTdXHRng25OeshrXK0idlQ-4XDf-SztG4heRKtDpffCZJGYXB1YNC4CbcQJIHlzL2zKYiAcP1jZFKJWZXsMEk7nAaPkjWs3lqJMmD_37zMP5Mlf4uY5oXo6P5_dmEX7g0eaPm2nzIP4EboHsKdnpPy5yN4k2kyy-UKIzgotTK5tSWiGhOPTAA9ikfpsBaZf7OYGumCLzHV9DF8Ovrw8f0xGRgWiGtasSEMo6SCO1M54Z2SQXIrlLUy2cFYh5pbynwVo48uJstolePUJhfKcOuS62XrJ7DTLbrwDMqkx5GFVjjc47BQKyORyTyGVsbKelXAbBxt7Qb4cWTB-KrzNkQJjfLRKJ90q5N8Cng1vbHsoTeuqfsOBTjVQ9Ds_GCx-qwHHdSVj1ZVjTToBDbStpQ5bo01iNGTlvAC9ka56UGT13oSWgHlVJpUEOMqpguLy7VWVKVxlJQW8LSfK1M7GDLqcJZK5NYs2mrodkk3_5JRvmvapg_LAl6P8-2qUf8ahufX9mAP7ubch3Qx-gJ2NqvLsA-33Y_NfL16mXXnFy9AH28 priority: 102 providerName: ProQuest – databaseName: SpringerLINK Contemporary 1997-Present dbid: RSV link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LaxRBEC5iVMgl8Z0xKnPwojCkp6e3uwdPRgweJIiPkFvTT13Q2bC7Efz3qep5yGoUFOYy0zXQVNeTqv4K4GngLQtN1FWKVlaC7uVYZUUVlHSeSe90bvk_fatOTvTZWftuC_h4FyZ3u48lyWyps1preVijGa0wQZnRTTBMn67BdXR2msY1vP9wOlUOCKN_LEde8deG-8ko_VeFlr93SP5SJs3e53jvf_Z9C3aHWLN82QvHbdiK3R242U-f_HEXXuSBmOX5kmo1dD4lBrAl4RdXgTD_e7yO0hNVNf_mqAnSx1BSU-k9-HT8-uOrN9UwS6Hys1auK071UCm8rb0MXquohJPaOYUWLzWxEY7xUKcUkk9oA532gjkMlqxwHoMs19yH7W7RxX0oUWMTj630lM3w2GiraGZ5iq1KtQu6gMORxcYPQOM07-KryQmHloZYYogl-GqQJQU8m_4470E2_kJ7RKc20RE8dv6wWH42g7aZOiSn65myFO7NlGsZ98JZZwmNB511AQfjmZtBZ1cGIyMaz8XqAsppFZWNKii2i4uLldFMIx8VYwU86AVk2gen2TmC44raEJ2NjW6udPMvGc-7ITHWqoDnowD93NSf2PDwX4gPYCc3PeDD2SPYXi8v4mO44b-v56vlk6w_l_h8FB0 priority: 102 providerName: Springer Nature |
| Title | Class prediction for high-dimensional class-imbalanced data |
| URI | https://link.springer.com/article/10.1186/1471-2105-11-523 https://www.ncbi.nlm.nih.gov/pubmed/20961420 https://www.proquest.com/docview/901869001 https://www.proquest.com/docview/808458700 https://pubmed.ncbi.nlm.nih.gov/PMC3098087 https://doaj.org/article/1dfb8157a264457b902c4baba0349212 |
| Volume | 11 |
| WOSCitedRecordID | wos000284532400002&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: PRVADU databaseName: BioMedCentral customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: RBZ dateStart: 20000101 isFulltext: true titleUrlDefault: https://www.biomedcentral.com/search/ providerName: BioMedCentral – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: DOA dateStart: 20000101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: M~E dateStart: 20000101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: P5Z dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: M7P dateStart: 20090101 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Computer Science Database customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: K7- dateStart: 20090101 isFulltext: true titleUrlDefault: http://search.proquest.com/compscijour providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: 7X7 dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: BENPR dateStart: 20090101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: PIMPY dateStart: 20090101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: RSV dateStart: 20001201 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1bi9QwFD7oquDL4nXtrg598EUhTJq2SYpPruyiqENZdRl9CbnigHaXmVnBf29O2qmO1xehBNqkQ-bLSXPCOfk-gIeONdSVXpLgNScVnsvRQlfECW4s5dbIlPJ_-krMZnI-b9ofpL4wJ6ynB-6BmxYuGFnUQuPKXQvTUGYro41GYhWW9IUZFc1mMzXED5CpP50rEgWJm5p6E6CUfDo-wxNlNSu3FqTE2_87Z_PXnMmfAqdpPTq-AbuDI5k_7f_ATbjku1twrZeW_HobniS1y_x8iYEYBD-P3mmO5MTEIaF_T8aRW2xFFp8NZjha73LMGL0D746P3j57TgahBGLrhq8Jw2Anr6wuLHdWCi8qw6UxIn7OQunLylDmihBcsCF-4Iy0FTURT10ZGz0oU96Fne6s8_cgj9MxMN9wi1sV5kupBQqSB9-IUBgnM5hu0FJ2YBFHMYtPKu0mJFeIr0J8462K-GbwaHzjvGfQ-EvbQxyAsR1yX6cH0SLUYBHqXxaRwcFm-NQwIVcquj2ovUWLDPKxNs4kDI_ozp9drJSkMuIoKM1grx_rsR8MhXEqFmvElhVsdXS7plt8TGTdJW3iD4sMHm_s5Xun_gTD_v-A4QCup0yHeDF6H3bWywv_AK7aL-vFajmBy2IuUikncOXwaNaeTNIUiuVLQSaYA9vGsq0_xPr2xev2fbw7eXP6DfZQHAc |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VAoIL70cojxzgAJK1jpO1HSGEeFWtdllxKKg3N37RlUp22d2C-qP4j3icB1oevfWAlEtix5rYn8fjzHg-gMeWldTmThLvKk4KPJdTiaogVnBtKDdaxpD_T2Mxmcj9_fLDBvzozsJgWGWnE6OitjOD_8gHYd1C8iSavZx_JUgahc7VjkGjQcXInXwPO7bli923YXifMLb9bu_NDmlJBYgZlnxFGDoGeWGqzHBrpHCi0FxqLcLU97nLC02Zzby33vigDLQ0BdXBaqgKbYK1ofPQ7jk4X-RS4LQaCdI7LZAeoPOESj7Igt4nYUc1xKNrQ5avrXyRIOBvVu2fwZm_eWjjwrd99T_rsmtwpbWw01fNlLgOG66-ARcbzs2Tm_A80oCm8wV6qBCVaTDbU8zaTCwyHTRZSlKDtcj0i8bQT-NsiqG0t-DjmUh-GzbrWe3uQhr0lGeu5Ab3cMzlshLI1O5dKXymrUxg0I2uMm16dWT5OFJxmyW5QjwoxEO4VQEPCTzt35g3qUVOqfsaAdPXw6Tg8cFs8Vm1OkZl1muZDUWFRu5Q6JIyU-hKV5iDKJgoCWx1OFGtplqqHiQJpH1pUDHoN6pqNzteKkll6EdBaQJ3Gmz2cjBkDCpYKBFrqF0TdL2knh7GLOY5LUPDIoFnHb5_CfWvbrh36hc8gks7e-_Harw7GW3B5RjnES5G78PmanHsHsAF8201XS4exnmbwsFZo_4nvkF88g |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1ZixQxEC50PfDFc9V2PfrBF4Uw6XQmSeOT16C4DAvqsm8hpw5ozzAzK_jvTaUPGV0FEfqlO5UmXalKqroqXwE89qyhvg6KxGAE4Xgux0jDiZfCOiqcVTnl__hQzufq5KQ56n-4bYZs9yEk2Z1pQJSmdjtZ-dipuBKTKi2pJDkrUzwVllyp83CBYxo9euvvj8coAuL1D6HJM3rtbEUZsf8sM_P3bMlfQqZ5J5pd-99vuA5Xexu0fN4JzQ04F9qbcKmrSvn9FjzLhTLL1RpjODhvZTJsS8Q1Jh5rAXQ4HqVDKrL4ajE50gVfYrLpPnycvf7w8g3paywQN23EljCMkwruTOWEd0oGya1Q1sq0EsY61NxS5qsYfXQxrY1WOU5tMqIMty4ZX7a-DXvtsg13oUyaHFlohEMvh4VaGYm1zGNoZKysVwVMBnZr1wOQYx2MLzo7IkpoZIlGlqRbnVhSwJOxx6oD3_gL7QucwZEOYbPzg-X6k-61UFc-WlVNpUEzcCptQ5nj1liDKD1pEy_gYJh_3evyRieLCct20aqAcmxNSoiRFdOG5elGK6oSHyWlBdzphGUcB8OaOpylFrkjRjsD3W1pF58zzndNm_RiWcDTQZh-DupPbLj3L8SP4PLRq5k-fDt_dwBXcl5Euhi9D3vb9Wl4ABfdt-1is36Y1eoHD7Af5Q |
| 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=Class+prediction+for+high-dimensional+class-imbalanced+data&rft.jtitle=BMC+bioinformatics&rft.au=Lusa+Lara&rft.au=Blagus+Rok&rft.date=2010-10-20&rft.pub=BMC&rft.issn=1471-2105&rft.eissn=1471-2105&rft.volume=11&rft.issue=1&rft.spage=523&rft_id=info:doi/10.1186%2F1471-2105-11-523&rft.externalDBID=DOA&rft.externalDocID=oai_doaj_org_article_1dfb8157a264457b902c4baba0349212 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1471-2105&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1471-2105&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1471-2105&client=summon |