A multi-objective evolutionary algorithm for robust positive-unlabeled learning
Positive and unlabeled (PU) learning is to learn a binary classifier with good generalization ability from PU data. A variety of PU learning algorithms with promising performance have been proposed. However, most of them assume that PU samples are “clean”, which is not true in real applications due...
Gespeichert in:
| Veröffentlicht in: | Information sciences Jg. 678; S. 120992 |
|---|---|
| Hauptverfasser: | , , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier Inc
01.09.2024
|
| Schlagworte: | |
| ISSN: | 0020-0255, 1872-6291 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Positive and unlabeled (PU) learning is to learn a binary classifier with good generalization ability from PU data. A variety of PU learning algorithms with promising performance have been proposed. However, most of them assume that PU samples are “clean”, which is not true in real applications due to the existing noisy or redundant samples. Thus, how to obtain a robust PU classifier with better performance is a challenging problem. To this end, we propose a novel multi-objective evolutionary algorithm to tackle it, named BPUSS-MOEA. Specifically, we firstly transform the robust PU learning into a bi-objective PU sample selection (BPUSS) problem, in which two objectives are designed. One is the number of selected “clean” PU samples and the other is the PU accuracy. Then, a dual-coding scheme is designed to represent the selected “clean” PU samples and the labels of U samples. With the dual-coding scheme, a novel offspring generation strategy is developed to achieve the offsprings with high quality. To further improve the performance of BPUSS-MOEA, an effective population initialization strategy is designed. Experiments on 10 datasets with different noise levels show that compared with the state-of-the-arts, the proposed algorithm demonstrates its robustness in terms of the PU accuracy. |
|---|---|
| AbstractList | Positive and unlabeled (PU) learning is to learn a binary classifier with good generalization ability from PU data. A variety of PU learning algorithms with promising performance have been proposed. However, most of them assume that PU samples are “clean”, which is not true in real applications due to the existing noisy or redundant samples. Thus, how to obtain a robust PU classifier with better performance is a challenging problem. To this end, we propose a novel multi-objective evolutionary algorithm to tackle it, named BPUSS-MOEA. Specifically, we firstly transform the robust PU learning into a bi-objective PU sample selection (BPUSS) problem, in which two objectives are designed. One is the number of selected “clean” PU samples and the other is the PU accuracy. Then, a dual-coding scheme is designed to represent the selected “clean” PU samples and the labels of U samples. With the dual-coding scheme, a novel offspring generation strategy is developed to achieve the offsprings with high quality. To further improve the performance of BPUSS-MOEA, an effective population initialization strategy is designed. Experiments on 10 datasets with different noise levels show that compared with the state-of-the-arts, the proposed algorithm demonstrates its robustness in terms of the PU accuracy. |
| ArticleNumber | 120992 |
| Author | Xie, Juan Tang, Qi Cai, Xiaoqiang Tan, Ming Li, Kaixuan Qiu, Jianfeng Cheng, Fan |
| Author_xml | – sequence: 1 givenname: Jianfeng surname: Qiu fullname: Qiu, Jianfeng email: qiujianf@ahu.edu.cn organization: Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei 230601, China – sequence: 2 givenname: Qi surname: Tang fullname: Tang, Qi email: 15856229622@163.com organization: School of Computer Science and Technology, Anhui University, Hefei 230601, China – sequence: 3 givenname: Ming surname: Tan fullname: Tan, Ming email: tanming@hfuu.edu.cn organization: School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China – sequence: 4 givenname: Kaixuan surname: Li fullname: Li, Kaixuan email: kxli@ahu.edu.cn organization: Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei 230601, China – sequence: 5 givenname: Juan surname: Xie fullname: Xie, Juan email: xiejuan0176@163.com organization: School of Mathematics and Physics, Anhui Jianzhu University, Hefei 230601, China – sequence: 6 givenname: Xiaoqiang surname: Cai fullname: Cai, Xiaoqiang email: saiqiang413@gmail.com organization: School of Computer Science and Technology, Anhui University, Hefei 230601, China – sequence: 7 givenname: Fan orcidid: 0000-0003-0175-0818 surname: Cheng fullname: Cheng, Fan email: chengfan@mail.ustc.edu.cn organization: Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei 230601, China |
| BookMark | eNp9kMtqwzAQAEVJoUnaD-jNP2BXWstyRE8h9AWBXNqzkOVVKuNIQbIN_fs6pOee9rKzzM6KLHzwSMgjowWjTDx1hfOpAAq8YEClhBuyZJsacgGSLciSUqA5haq6I6uUOkopr4VYksM2O4394PLQdGgGN2GGU-jHwQWv40-m-2OIbvg-ZTbELIZmTEN2DsldVvPR97rBHtusRx2988d7cmt1n_Dhb67J1-vL5-493x_ePnbbfW6AyyFnRvPStEYAaI1ctxsAaXlZNkJKK2pLhZyNeVVCYxlaVrfMmLrmQEVly7ZcE3a9a2JIKaJV5-hOs7FiVF2KqE7NRdSliLoWmZnnK4Oz2OQwqmQceoOti_Pvqg3uH_oXq61sQA |
| Cites_doi | 10.1016/j.asoc.2021.107794 10.1007/s11063-021-10590-y 10.1002/int.22437 10.1145/3340848 10.1109/TPAMI.2018.2860995 10.1007/s10994-021-06111-6 10.1016/j.asoc.2020.106986 10.1109/TPAMI.2019.2941684 10.1109/TEVC.2014.2308305 10.1109/4235.996017 10.1016/j.eswa.2021.116232 10.1016/j.ipm.2017.02.008 10.1109/TEVC.2021.3051608 10.1007/s10994-020-05877-5 10.1504/IJBIC.2016.076329 10.1109/TGRS.2019.2892567 10.1016/j.ins.2021.08.099 10.1109/TNNLS.2018.2870666 10.1016/j.swevo.2022.101174 10.1016/j.ins.2021.01.002 10.1093/bioinformatics/btv550 10.1016/j.compbiolchem.2021.107566 |
| ContentType | Journal Article |
| Copyright | 2024 Elsevier Inc. |
| Copyright_xml | – notice: 2024 Elsevier Inc. |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.ins.2024.120992 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Library & Information Science |
| EISSN | 1872-6291 |
| ExternalDocumentID | 10_1016_j_ins_2024_120992 S002002552400906X |
| GroupedDBID | --K --M --Z -~X .DC .~1 0R~ 1B1 1OL 1RT 1~. 1~5 29I 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN 9JO AAAKF AAAKG AABNK AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AARIN AAXUO AAYFN ABAOU ABBOA ABEFU ABFNM ABJNI ABMAC ABTAH ABUCO ABXDB ACDAQ ACGFS ACNNM ACRLP ACZNC ADBBV ADEZE ADGUI ADJOM ADMUD ADTZH AEBSH AECPX AEKER AENEX AFFNX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIGVJ AIKHN AITUG AJOXV AKRWK ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD APLSM ARUGR ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ HAMUX HLZ HVGLF HZ~ H~9 IHE J1W JJJVA KOM LG9 LY1 M41 MHUIS MO0 MS~ N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SBC SDF SDG SDP SDS SES SEW SPC SPCBC SSB SSD SST SSV SSW SSZ T5K TN5 TWZ UHS WH7 WUQ XPP YYP ZMT ZY4 ~02 ~G- 77I 9DU AATTM AAXKI AAYWO AAYXX ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO ADVLN AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKYEP ANKPU APXCP CITATION EFKBS EFLBG ~HD |
| ID | FETCH-LOGICAL-c249t-1ca43cdc622aae4ad8229f433b699f67f0690204532bf1ef17d1cc7742065f3d3 |
| ISICitedReferencesCount | 0 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001302628900001&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 | Sat Nov 29 02:44:14 EST 2025 Sat Jun 29 15:30:23 EDT 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Robust PU learning PU sample selection Multi-objective evolutionary algorithm Dual-coding scheme |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c249t-1ca43cdc622aae4ad8229f433b699f67f0690204532bf1ef17d1cc7742065f3d3 |
| ORCID | 0000-0003-0175-0818 |
| ParticipantIDs | crossref_primary_10_1016_j_ins_2024_120992 elsevier_sciencedirect_doi_10_1016_j_ins_2024_120992 |
| PublicationCentury | 2000 |
| PublicationDate | September 2024 2024-09-00 |
| PublicationDateYYYYMMDD | 2024-09-01 |
| PublicationDate_xml | – month: 09 year: 2024 text: September 2024 |
| PublicationDecade | 2020 |
| PublicationTitle | Information sciences |
| PublicationYear | 2024 |
| Publisher | Elsevier Inc |
| Publisher_xml | – name: Elsevier Inc |
| References | Zhang, Sun, Cheng, Ma, Sun (br0380) 2020 Su, Chen, Xu (br0280) 2021 Zhao, Pang, Liu, Ye (br0410) 2021 Carnevali, Rossi, Milios, de Andrade Lopes (br0030) 2021; 580 Kalyanmoy, Jain (br0070) 2013; 18 Wang, Lin, Bi, Sun, Si, Liu (br0310) 2022; 191 Sansone, De Natale, Zhou (br0260) 2018; 41 Liu, Lee, Yu, Li (br0160) 2002 Caravanti de Souza, Nogueira, Rossi, Marcacini, dos Santos, Rezende (br0060) 2021; 111 Luo, Zhao, Chen, Qiao, Du, Zhang, Wu, Cai, He, Rajmohan, Lin (br0170) 2021 Ren, Yang, Zhao, Chen, Xue, Miao, Huang, Liu (br0250) 2019; 30 Xue, Xue, Zhang (br0350) 2019; 13 Zhang, Ren, Liu, Yang, Gong (br0370) 2019 Wu, Zhan, Zhang (br0320) 2021; 25 Li, Liu (br0150) 2003 Lee, Liu (br0130) 2003 Zhao, Xu, Jiang, Wen, Huang (br0400) 2022 Nguyen, Li, Ng (br0190) 2011 Du Plessis, Niu, Sugiyama (br0090) 2014 Qiu, Cai, Zhang, Cheng (br0210) 2022; 75 Xiang, Tian, Xiao, Zhang (br0340) 2019 Yang, Humphrey, James, Yang, Jothi (br0360) 2016; 32 Mu, Sun, Yuan, Shi (br0180) 2021; 36 Wang, Peng, He, Liu, Li (br0300) 2021; 53 Gong, Shi, Liu, Zhang, Yang, Tao (br0120) 2021; 43 Van Der Maaten (br0290) 2014; 15 Du Plessis, Niu, Sugiyama (br0100) 2015 Deb, Pratap, Agarwal, Meyarivan (br0080) 2002; 6 Cai, Ma, Gong, Tian (br0020) 2016; 8 Shi, Pan, Yang, Gong (br0270) 2018 Xuxi Chen, Wuyang Chen, Tianlong Chen, Ye Yuan, Chen Gong, Kewei Chen, Zhangyang Wang, Self-PU: Self boosted and calibrated positive-unlabeled training, 2020, pp. 1510–1519. Qiu, Xiang, Wang, Zhang (br0240) 2021; 112 Zhang, Tian, Cheng, Jin (br0390) 2014; 19 Qiu, Cai, Zhang, Cheng, Yuan, Fu (br0220) 2021; 101 Onan, Korukoğlu, Bulut (br0200) 2017; 53 Gao, Pan, Zuo, Gao, Xu (br0110) 2019; 57 Bekker, Davis (br0010) 2020; 109 Li, Wang, Ma, Ortal, Zhao, Stenger, Hirate (br0140) 2019 Qiu, Cai, Zhang, Cheng, Yuan, Fu (br0230) 2021; 101 Chen, Gong, Yang (br0040) 2021; 558 Zheng, Peng, Zhang, Zhao, Gao, Li (br0420) 2019; 20 Wu, Zhu, Wang, Zhang (br0330) 2021; 95 Deb (10.1016/j.ins.2024.120992_br0080) 2002; 6 Cai (10.1016/j.ins.2024.120992_br0020) 2016; 8 Kalyanmoy (10.1016/j.ins.2024.120992_br0070) 2013; 18 Qiu (10.1016/j.ins.2024.120992_br0230) 2021; 101 Xiang (10.1016/j.ins.2024.120992_br0340) 2019 Li (10.1016/j.ins.2024.120992_br0150) 2003 Qiu (10.1016/j.ins.2024.120992_br0220) 2021; 101 Yang (10.1016/j.ins.2024.120992_br0360) 2016; 32 Gong (10.1016/j.ins.2024.120992_br0120) 2021; 43 Liu (10.1016/j.ins.2024.120992_br0160) 2002 Chen (10.1016/j.ins.2024.120992_br0040) 2021; 558 10.1016/j.ins.2024.120992_br0050 Du Plessis (10.1016/j.ins.2024.120992_br0090) 2014 Zhang (10.1016/j.ins.2024.120992_br0370) 2019 Zheng (10.1016/j.ins.2024.120992_br0420) 2019; 20 Shi (10.1016/j.ins.2024.120992_br0270) 2018 Onan (10.1016/j.ins.2024.120992_br0200) 2017; 53 Su (10.1016/j.ins.2024.120992_br0280) 2021 Wang (10.1016/j.ins.2024.120992_br0300) 2021; 53 Nguyen (10.1016/j.ins.2024.120992_br0190) 2011 Bekker (10.1016/j.ins.2024.120992_br0010) 2020; 109 Wu (10.1016/j.ins.2024.120992_br0320) 2021; 25 Carnevali (10.1016/j.ins.2024.120992_br0030) 2021; 580 Wang (10.1016/j.ins.2024.120992_br0310) 2022; 191 Zhao (10.1016/j.ins.2024.120992_br0400) 2022 Caravanti de Souza (10.1016/j.ins.2024.120992_br0060) 2021; 111 Zhao (10.1016/j.ins.2024.120992_br0410) 2021 Du Plessis (10.1016/j.ins.2024.120992_br0100) 2015 Wu (10.1016/j.ins.2024.120992_br0330) 2021; 95 Zhang (10.1016/j.ins.2024.120992_br0380) 2020 Li (10.1016/j.ins.2024.120992_br0140) 2019 Luo (10.1016/j.ins.2024.120992_br0170) 2021 Qiu (10.1016/j.ins.2024.120992_br0240) 2021; 112 Ren (10.1016/j.ins.2024.120992_br0250) 2019; 30 Zhang (10.1016/j.ins.2024.120992_br0390) 2014; 19 Sansone (10.1016/j.ins.2024.120992_br0260) 2018; 41 Gao (10.1016/j.ins.2024.120992_br0110) 2019; 57 Xue (10.1016/j.ins.2024.120992_br0350) 2019; 13 Lee (10.1016/j.ins.2024.120992_br0130) 2003 Van Der Maaten (10.1016/j.ins.2024.120992_br0290) 2014; 15 Mu (10.1016/j.ins.2024.120992_br0180) 2021; 36 Qiu (10.1016/j.ins.2024.120992_br0210) 2022; 75 |
| References_xml | – start-page: 1421 year: 2011 end-page: 1426 ident: br0190 article-title: Positive unlabeled learning for time series classification publication-title: International Joint Conference on Artificial Intelligence – volume: 101 start-page: 1 year: 2021 end-page: 14 ident: br0220 article-title: An evolutionary multi-objective approach to learn from positive and unlabeled data publication-title: Appl. Soft Comput. – volume: 580 start-page: 655 year: 2021 end-page: 672 ident: br0030 article-title: A graph-based approach for positive and unlabeled learning publication-title: Inf. Sci. – volume: 41 start-page: 2584 year: 2018 end-page: 2598 ident: br0260 article-title: Efficient training for positive unlabeled learning publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – start-page: 2995 year: 2021 end-page: 3001 ident: br0280 article-title: Positive-unlabeled learning from imbalanced data publication-title: International Joint Conference on Artificial Intelligence – volume: 13 start-page: 1 year: 2019 end-page: 27 ident: br0350 article-title: Self-adaptive particle swarm optimization for large-scale feature selection in classification publication-title: ACM Trans. Knowl. Discov. Data – volume: 57 start-page: 4720 year: 2019 end-page: 4734 ident: br0110 article-title: An optimized deep network representation of multimutation differential evolution and its application in seismic inversion publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 19 start-page: 201 year: 2014 end-page: 213 ident: br0390 article-title: An efficient approach to nondominated sorting for evolutionary multiobjective optimization publication-title: IEEE Trans. Evol. Comput. – volume: 18 start-page: 577 year: 2013 end-page: 601 ident: br0070 article-title: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints publication-title: IEEE Trans. Evol. Comput. – volume: 36 start-page: 3767 year: 2021 end-page: 3788 ident: br0180 article-title: Positive unlabeled learning-based anomaly detection in videos publication-title: Int. J. Intell. Syst. – volume: 20 start-page: 1 year: 2019 end-page: 12 ident: br0420 article-title: DDI-PULearn: a positive-unlabeled learning method for large-scale prediction of drug-drug interactions publication-title: BMC Bioinform. – year: 2019 ident: br0340 article-title: A clustering-based surrogate-assisted multi-objective evolutionary algorithm for shelter location under uncertainty of road networks publication-title: IEEE Trans. Ind. Inform. – volume: 558 start-page: 229 year: 2021 end-page: 245 ident: br0040 article-title: Cost-sensitive positive and unlabeled learning publication-title: Inf. Sci. – volume: 111 start-page: 3549 year: 2021 end-page: 3592 ident: br0060 article-title: A network-based positive and unlabeled learning approach for fake news detection publication-title: Mach. Learn. – start-page: 2689 year: 2018 end-page: 2695 ident: br0270 article-title: Positive and unlabeled learning via loss decomposition and centroid estimation publication-title: International Joint Conference on Artificial Intelligence – volume: 191 year: 2022 ident: br0310 article-title: Adaptive multi-task positive-unlabeled learning for joint prediction of multiple chronic diseases using online shopping behaviors publication-title: Expert Syst. Appl. – start-page: 387 year: 2002 end-page: 394 ident: br0160 article-title: Partially supervised classification of text documents publication-title: International Conference on Machine Learning – volume: 32 start-page: 252 year: 2016 end-page: 259 ident: br0360 article-title: Positive-unlabeled ensemble learning for kinase substrate prediction from dynamic phosphoproteomics data publication-title: Bioinformatics – volume: 6 start-page: 182 year: 2002 end-page: 197 ident: br0080 article-title: A fast and elitist multiobjective genetic algorithm: NSGA-II publication-title: IEEE Trans. Evol. Comput. – start-page: 4250 year: 2019 end-page: 4256 ident: br0370 article-title: Positive and unlabeled learning with label disambiguation publication-title: International Joint Conference on Artificial Intelligence – start-page: 8784 year: 2021 end-page: 8792 ident: br0170 article-title: PULNS: positive-unlabeled learning with effective negative sample selector publication-title: AAAI Conference on Artificial Intelligence – start-page: 14441 year: 2022 end-page: 14450 ident: br0400 article-title: Dist-PU: positive-unlabeled learning from a label distribution perspective publication-title: IEEE Conference on Computer Vision and Pattern Recognition – start-page: 509 year: 2021 end-page: 518 ident: br0410 article-title: Positive-unlabeled learning for cell detection in histopathology images with incomplete annotations publication-title: Medical Image Computing and Computer Assisted Intervention – volume: 8 start-page: 84 year: 2016 end-page: 98 ident: br0020 article-title: A survey on network community detection based on evolutionary computation publication-title: Int. J. Bio-Inspir. Comput. – volume: 53 start-page: 4239 year: 2021 end-page: 4255 ident: br0300 article-title: A two-step classification method based on collaborative representation for positive and unlabeled learning publication-title: Neural Process. Lett. – start-page: 399 year: 2019 end-page: 408 ident: br0140 article-title: Learning classifiers on positive and unlabeled data with policy gradient publication-title: IEEE International Conference on Data Mining – volume: 53 start-page: 814 year: 2017 end-page: 833 ident: br0200 article-title: A hybrid ensemble pruning approach based on consensus clustering and multi-objective evolutionary algorithm for sentiment classification publication-title: Inf. Process. Manag. – volume: 109 start-page: 719 year: 2020 end-page: 760 ident: br0010 article-title: Learning from positive and unlabeled data: a survey publication-title: Mach. Learn. – volume: 112 start-page: 1 year: 2021 end-page: 14 ident: br0240 article-title: A multi-objective feature selection approach based on chemical reaction optimization publication-title: Appl. Soft Comput. – volume: 15 start-page: 3221 year: 2014 end-page: 3245 ident: br0290 article-title: Accelerating t-SNE using tree-based algorithms publication-title: J. Mach. Learn. Res. – volume: 43 start-page: 918 year: 2021 end-page: 932 ident: br0120 article-title: Loss decomposition and centroid estimation for positive and unlabeled learning publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – start-page: 587 year: 2003 end-page: 592 ident: br0150 article-title: Learning to classify texts using positive and unlabeled data publication-title: International Joint Conference on Artificial Intelligence – start-page: 1386 year: 2015 end-page: 1394 ident: br0100 article-title: Convex formulation for learning from positive and unlabeled data publication-title: International Conference on Machine Learning – start-page: 448 year: 2003 end-page: 455 ident: br0130 article-title: Learning with positive and unlabeled examples using weighted logistic regression publication-title: International Conference on Machine Learning – volume: 30 start-page: 3072 year: 2019 end-page: 3083 ident: br0250 article-title: A robust AUC maximization framework with simultaneous outlier detection and feature selection for positive-unlabeled classification publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 101 year: 2021 ident: br0230 article-title: An evolutionary multi-objective approach to learn from positive and unlabeled data publication-title: Appl. Soft Comput. – reference: Xuxi Chen, Wuyang Chen, Tianlong Chen, Ye Yuan, Chen Gong, Kewei Chen, Zhangyang Wang, Self-PU: Self boosted and calibrated positive-unlabeled training, 2020, pp. 1510–1519. – volume: 25 start-page: 478 year: 2021 end-page: 491 ident: br0320 article-title: Safe: scale-adaptive fitness evaluation method for expensive optimization problems publication-title: IEEE Trans. Evol. Comput. – volume: 95 start-page: 1 year: 2021 end-page: 13 ident: br0330 article-title: An ensemble learning framework for potential mirna-disease association prediction with positive-unlabeled data publication-title: Comput. Biol. Chem. – start-page: 1 year: 2020 end-page: 8 ident: br0380 article-title: An overlapping community detection based multi-objective evolutionary algorithm for diversified social influence maximization publication-title: IEEE Congress on Evolutionary Computation – start-page: 703 year: 2014 end-page: 711 ident: br0090 article-title: Analysis of learning from positive and unlabeled data publication-title: Advances in Neural Information Processing Systems – volume: 75 year: 2022 ident: br0210 article-title: A loss matrix-based alternating optimization method for sparse pu learning publication-title: Swarm Evol. Comput. – start-page: 1421 year: 2011 ident: 10.1016/j.ins.2024.120992_br0190 article-title: Positive unlabeled learning for time series classification – start-page: 587 year: 2003 ident: 10.1016/j.ins.2024.120992_br0150 article-title: Learning to classify texts using positive and unlabeled data – start-page: 14441 year: 2022 ident: 10.1016/j.ins.2024.120992_br0400 article-title: Dist-PU: positive-unlabeled learning from a label distribution perspective – volume: 112 start-page: 1 year: 2021 ident: 10.1016/j.ins.2024.120992_br0240 article-title: A multi-objective feature selection approach based on chemical reaction optimization publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2021.107794 – volume: 53 start-page: 4239 year: 2021 ident: 10.1016/j.ins.2024.120992_br0300 article-title: A two-step classification method based on collaborative representation for positive and unlabeled learning publication-title: Neural Process. Lett. doi: 10.1007/s11063-021-10590-y – volume: 36 start-page: 3767 issue: 8 year: 2021 ident: 10.1016/j.ins.2024.120992_br0180 article-title: Positive unlabeled learning-based anomaly detection in videos publication-title: Int. J. Intell. Syst. doi: 10.1002/int.22437 – volume: 13 start-page: 1 issue: 5 year: 2019 ident: 10.1016/j.ins.2024.120992_br0350 article-title: Self-adaptive particle swarm optimization for large-scale feature selection in classification publication-title: ACM Trans. Knowl. Discov. Data doi: 10.1145/3340848 – start-page: 509 year: 2021 ident: 10.1016/j.ins.2024.120992_br0410 article-title: Positive-unlabeled learning for cell detection in histopathology images with incomplete annotations – start-page: 399 year: 2019 ident: 10.1016/j.ins.2024.120992_br0140 article-title: Learning classifiers on positive and unlabeled data with policy gradient – start-page: 8784 year: 2021 ident: 10.1016/j.ins.2024.120992_br0170 article-title: PULNS: positive-unlabeled learning with effective negative sample selector – start-page: 387 year: 2002 ident: 10.1016/j.ins.2024.120992_br0160 article-title: Partially supervised classification of text documents – year: 2019 ident: 10.1016/j.ins.2024.120992_br0340 article-title: A clustering-based surrogate-assisted multi-objective evolutionary algorithm for shelter location under uncertainty of road networks publication-title: IEEE Trans. Ind. Inform. – volume: 41 start-page: 2584 issue: 11 year: 2018 ident: 10.1016/j.ins.2024.120992_br0260 article-title: Efficient training for positive unlabeled learning publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2018.2860995 – start-page: 1 year: 2020 ident: 10.1016/j.ins.2024.120992_br0380 article-title: An overlapping community detection based multi-objective evolutionary algorithm for diversified social influence maximization – volume: 111 start-page: 3549 year: 2021 ident: 10.1016/j.ins.2024.120992_br0060 article-title: A network-based positive and unlabeled learning approach for fake news detection publication-title: Mach. Learn. doi: 10.1007/s10994-021-06111-6 – volume: 101 start-page: 1 year: 2021 ident: 10.1016/j.ins.2024.120992_br0220 article-title: An evolutionary multi-objective approach to learn from positive and unlabeled data publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2020.106986 – volume: 43 start-page: 918 issue: 3 year: 2021 ident: 10.1016/j.ins.2024.120992_br0120 article-title: Loss decomposition and centroid estimation for positive and unlabeled learning publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2019.2941684 – volume: 101 year: 2021 ident: 10.1016/j.ins.2024.120992_br0230 article-title: An evolutionary multi-objective approach to learn from positive and unlabeled data publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2020.106986 – volume: 19 start-page: 201 issue: 2 year: 2014 ident: 10.1016/j.ins.2024.120992_br0390 article-title: An efficient approach to nondominated sorting for evolutionary multiobjective optimization publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2014.2308305 – volume: 6 start-page: 182 issue: 2 year: 2002 ident: 10.1016/j.ins.2024.120992_br0080 article-title: A fast and elitist multiobjective genetic algorithm: NSGA-II publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/4235.996017 – volume: 18 start-page: 577 issue: 4 year: 2013 ident: 10.1016/j.ins.2024.120992_br0070 article-title: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints publication-title: IEEE Trans. Evol. Comput. – volume: 191 year: 2022 ident: 10.1016/j.ins.2024.120992_br0310 article-title: Adaptive multi-task positive-unlabeled learning for joint prediction of multiple chronic diseases using online shopping behaviors publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2021.116232 – volume: 53 start-page: 814 issue: 4 year: 2017 ident: 10.1016/j.ins.2024.120992_br0200 article-title: A hybrid ensemble pruning approach based on consensus clustering and multi-objective evolutionary algorithm for sentiment classification publication-title: Inf. Process. Manag. doi: 10.1016/j.ipm.2017.02.008 – volume: 25 start-page: 478 issue: 3 year: 2021 ident: 10.1016/j.ins.2024.120992_br0320 article-title: Safe: scale-adaptive fitness evaluation method for expensive optimization problems publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2021.3051608 – start-page: 448 year: 2003 ident: 10.1016/j.ins.2024.120992_br0130 article-title: Learning with positive and unlabeled examples using weighted logistic regression – start-page: 2995 year: 2021 ident: 10.1016/j.ins.2024.120992_br0280 article-title: Positive-unlabeled learning from imbalanced data – volume: 109 start-page: 719 year: 2020 ident: 10.1016/j.ins.2024.120992_br0010 article-title: Learning from positive and unlabeled data: a survey publication-title: Mach. Learn. doi: 10.1007/s10994-020-05877-5 – volume: 8 start-page: 84 issue: 2 year: 2016 ident: 10.1016/j.ins.2024.120992_br0020 article-title: A survey on network community detection based on evolutionary computation publication-title: Int. J. Bio-Inspir. Comput. doi: 10.1504/IJBIC.2016.076329 – start-page: 2689 year: 2018 ident: 10.1016/j.ins.2024.120992_br0270 article-title: Positive and unlabeled learning via loss decomposition and centroid estimation – volume: 15 start-page: 3221 issue: 1 year: 2014 ident: 10.1016/j.ins.2024.120992_br0290 article-title: Accelerating t-SNE using tree-based algorithms publication-title: J. Mach. Learn. Res. – volume: 20 start-page: 1 issue: 661 year: 2019 ident: 10.1016/j.ins.2024.120992_br0420 article-title: DDI-PULearn: a positive-unlabeled learning method for large-scale prediction of drug-drug interactions publication-title: BMC Bioinform. – start-page: 4250 year: 2019 ident: 10.1016/j.ins.2024.120992_br0370 article-title: Positive and unlabeled learning with label disambiguation – start-page: 703 year: 2014 ident: 10.1016/j.ins.2024.120992_br0090 article-title: Analysis of learning from positive and unlabeled data – volume: 57 start-page: 4720 issue: 7 year: 2019 ident: 10.1016/j.ins.2024.120992_br0110 article-title: An optimized deep network representation of multimutation differential evolution and its application in seismic inversion publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2019.2892567 – volume: 580 start-page: 655 year: 2021 ident: 10.1016/j.ins.2024.120992_br0030 article-title: A graph-based approach for positive and unlabeled learning publication-title: Inf. Sci. doi: 10.1016/j.ins.2021.08.099 – volume: 30 start-page: 3072 issue: 10 year: 2019 ident: 10.1016/j.ins.2024.120992_br0250 article-title: A robust AUC maximization framework with simultaneous outlier detection and feature selection for positive-unlabeled classification publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2018.2870666 – volume: 75 year: 2022 ident: 10.1016/j.ins.2024.120992_br0210 article-title: A loss matrix-based alternating optimization method for sparse pu learning publication-title: Swarm Evol. Comput. doi: 10.1016/j.swevo.2022.101174 – volume: 558 start-page: 229 year: 2021 ident: 10.1016/j.ins.2024.120992_br0040 article-title: Cost-sensitive positive and unlabeled learning publication-title: Inf. Sci. doi: 10.1016/j.ins.2021.01.002 – ident: 10.1016/j.ins.2024.120992_br0050 – start-page: 1386 year: 2015 ident: 10.1016/j.ins.2024.120992_br0100 article-title: Convex formulation for learning from positive and unlabeled data – volume: 32 start-page: 252 issue: 2 year: 2016 ident: 10.1016/j.ins.2024.120992_br0360 article-title: Positive-unlabeled ensemble learning for kinase substrate prediction from dynamic phosphoproteomics data publication-title: Bioinformatics doi: 10.1093/bioinformatics/btv550 – volume: 95 start-page: 1 year: 2021 ident: 10.1016/j.ins.2024.120992_br0330 article-title: An ensemble learning framework for potential mirna-disease association prediction with positive-unlabeled data publication-title: Comput. Biol. Chem. doi: 10.1016/j.compbiolchem.2021.107566 |
| SSID | ssj0004766 |
| Score | 2.4424007 |
| Snippet | Positive and unlabeled (PU) learning is to learn a binary classifier with good generalization ability from PU data. A variety of PU learning algorithms with... |
| SourceID | crossref elsevier |
| SourceType | Index Database Publisher |
| StartPage | 120992 |
| SubjectTerms | Dual-coding scheme Multi-objective evolutionary algorithm PU sample selection Robust PU learning |
| Title | A multi-objective evolutionary algorithm for robust positive-unlabeled learning |
| URI | https://dx.doi.org/10.1016/j.ins.2024.120992 |
| Volume | 678 |
| WOSCitedRecordID | wos001302628900001&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/eLvHCXMwtV1Rb9MwELZKxwM8IBggBmzyA-KByQg7qR0_VmhoQ9NgYqC-RY5jj1Qjnbqk6s_nHNtp2JgESLxEkVUn1d2X85393R1Cr4xL7eJ6QqQSkqSqNCRLbAbGkJXaUFqo7sT027E4OclmM_l5NPoSc2FWF6Kus_VaXv5XVcMYKNulzv6FuvuHwgDcg9LhCmqH6x8pfupJgmRRzL0x2zer8ELHkFMX54tl1Xz_0REMl4uivWr2PXVrZUhbAypgJSpjO4nzofcacpc6yISls3fJT6u2QwTAzZowrdsR8MbktBqMeL7-5kfHnlKgqnUbwBr2IVjaE63C5lhMkPmFv-m8UeLCFr_ceBubCUY48026ohHmvpHPDYPu9xbmEIW42uosfdvl-rLN6tVzCt2xcxchOVasfMdnd9AWExOZjdHW9Ohg9nGTLiv8EXb8b_Gwu6P9XXvR792VgQty9hA9CLEDnnqdP0IjU2-j-4OKkttoN-Sh4Nd4oCwcLPhj9GmKr6EDD9GBe3RgmIw9OvBNdOCIjifo64eDs_eHJPTUIBoC7YZQrdJEl5ozppSBb9MV_LdpkhRcSsuFdZWrXYuChBWWGktFSbWGGIGBr2qTMnmKxvWiNs8QLgR81FQpcIJYariSWhecKq5LKyeCyh30Joouv_SlU_LIKZznIOfcyTn3ct5BaRRuHgDsfbockHD7tOf_Nu0FureB8Es0bpat2UV39aqprpZ7AS8_AfYdf6A |
| 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=A+multi-objective+evolutionary+algorithm+for+robust+positive-unlabeled+learning&rft.jtitle=Information+sciences&rft.au=Qiu%2C+Jianfeng&rft.au=Tang%2C+Qi&rft.au=Tan%2C+Ming&rft.au=Li%2C+Kaixuan&rft.date=2024-09-01&rft.pub=Elsevier+Inc&rft.issn=0020-0255&rft.eissn=1872-6291&rft.volume=678&rft_id=info:doi/10.1016%2Fj.ins.2024.120992&rft.externalDocID=S002002552400906X |
| 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 |