{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T15:06:23Z","timestamp":1780931183979,"version":"3.54.1"},"reference-count":48,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Pattern Recognition"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.patcog.2026.113972","type":"journal-article","created":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T15:12:10Z","timestamp":1779203530000},"page":"113972","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PA","title":["An overlap-driven feature selection via volume-aware separability-consistency index for imbalanced classification"],"prefix":"10.1016","volume":"180","author":[{"given":"Yanzhou","family":"Pan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minghao","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weihua","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"10","key":"10.1016\/j.patcog.2026.113972_b1","doi-asserted-by":"crossref","first-page":"5194","DOI":"10.1109\/TKDE.2024.3384274","article-title":"Improved contraction-expansion subspace ensemble for high-dimensional imbalanced data classification","volume":"36","author":"Xu","year":"2024","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.patcog.2026.113972_b2","article-title":"Understanding and tackling the modality imbalance problem in multimodal survival prediction","volume":"172","author":"Zhou","year":"2025","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.113972_b3","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2025.111839","article-title":"AGFormer: An anchor-guided transformer for class imbalance in remote sensing change detection","volume":"168","author":"Chen","year":"2025","journal-title":"Pattern Recognit."},{"issue":"9","key":"10.1016\/j.patcog.2026.113972_b4","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","article-title":"Learning from imbalanced data","volume":"21","author":"He","year":"2009","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"9","key":"10.1016\/j.patcog.2026.113972_b5","doi-asserted-by":"crossref","first-page":"11671","DOI":"10.1109\/TNNLS.2024.3353531","article-title":"Graph embedded intuitionistic fuzzy random vector functional link neural network for class imbalance learning","volume":"35","author":"Ganaie","year":"2024","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.patcog.2026.113972_b6","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2022.109853","article-title":"The effects of data balancing approaches: A case study","volume":"132","author":"Mooijman","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.patcog.2026.113972_b7","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2020.106631","article-title":"On the class overlap problem in imbalanced data classification","volume":"212","author":"Vuttipittayamongkol","year":"2021","journal-title":"Knowl. -Based Syst."},{"key":"10.1016\/j.patcog.2026.113972_b8","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.inffus.2022.08.017","article-title":"A unifying view of class overlap and imbalance: Key concepts, multi-view panorama, and open avenues for research","volume":"89","author":"Santos","year":"2023","journal-title":"Inf. Fusion"},{"issue":"4","key":"10.1016\/j.patcog.2026.113972_b9","doi-asserted-by":"crossref","first-page":"1130","DOI":"10.1109\/TEVC.2022.3203862","article-title":"Detecting overlapping areas in unbalanced high-dimensional data using neighborhood rough set and genetic programming","volume":"27","author":"Pei","year":"2023","journal-title":"IEEE Trans. Evol. Comput."},{"key":"10.1016\/j.patcog.2026.113972_b10","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.119735","article-title":"Class-overlap undersampling based on schur decomposition for class-imbalance problems","volume":"221","author":"Dai","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.patcog.2026.113972_b11","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2024.111904","article-title":"Distance mapping overlap complexity metric for class-imbalance problems","volume":"163","author":"Dai","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.patcog.2026.113972_b12","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.eswa.2018.01.008","article-title":"An overlap-sensitive margin classifier for imbalanced and overlapping data","volume":"98","author":"Lee","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.patcog.2026.113972_b13","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.eswa.2016.12.035","article-title":"Learning from class-imbalanced data: Review of methods and applications","volume":"73","author":"Guo","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.patcog.2026.113972_b14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ins.2019.01.041","article-title":"Feature selection for imbalanced data based on neighborhood rough sets","volume":"483","author":"Chen","year":"2019","journal-title":"Inf. Sci."},{"issue":"1","key":"10.1016\/j.patcog.2026.113972_b15","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1109\/TAI.2023.3237203","article-title":"Feature selection for unbalanced distribution hybrid data based on k-nearest neighborhood rough set","volume":"5","author":"Xu","year":"2024","journal-title":"IEEE Trans. Artif. Intell."},{"key":"10.1016\/j.patcog.2026.113972_b16","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.inffus.2023.02.016","article-title":"TFSFB: Two-stage feature selection via fusing fuzzy multi-neighborhood rough set with binary whale optimization for imbalanced data","volume":"95","author":"Sun","year":"2023","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.patcog.2026.113972_b17","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2022.109849","article-title":"TSFNFR: Two-stage fuzzy neighborhood-based feature reduction with binary whale optimization algorithm for imbalanced data classification","volume":"256","author":"Sun","year":"2022","journal-title":"Knowl. -Based Syst."},{"issue":"8","key":"10.1016\/j.patcog.2026.113972_b18","doi-asserted-by":"crossref","first-page":"5717","DOI":"10.1109\/TSMC.2025.3573080","article-title":"A multiform framework for multiobjective feature selection in unbalanced classification: combining oversampling and cost-sensitive learning","volume":"55","author":"Liang","year":"2025","journal-title":"IEEE Trans. Syst. Man, Cybern. Syst."},{"key":"10.1016\/j.patcog.2026.113972_b19","article-title":"Supervised incremental feature selection using regularization vector for dynamic multi-scale interval valued datasets","volume":"170","author":"Feng","year":"2025","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.113972_b20","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2024.102426","article-title":"Concept-cognitive learning survey: Mining and fusing knowledge from data","volume":"109","author":"Guo","year":"2024","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.patcog.2026.113972_b21","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2025.112268","article-title":"CM-CCL: Collaborative multi-scale concept-cognitive learning for knowledge discovery","volume":"171","author":"Xu","year":"2026","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.113972_b22","doi-asserted-by":"crossref","DOI":"10.1109\/TFUZZ.2026.3664190","article-title":"Adaptive granular-ball concept-cognitive learning for efficient and robust fuzzy knowledge representation in classification tasks","author":"Guo","year":"2026","journal-title":"IEEE Trans. Fuzzy Syst."},{"issue":"4","key":"10.1016\/j.patcog.2026.113972_b23","doi-asserted-by":"crossref","first-page":"4587","DOI":"10.1109\/TPAMI.2025.3647921","article-title":"Robust semi-supervised feature selection with multi-granularity zentropy modeling","volume":"48","author":"Yuan","year":"2026","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"11","key":"10.1016\/j.patcog.2026.113972_b24","doi-asserted-by":"crossref","first-page":"7326","DOI":"10.1109\/TKDE.2024.3419215","article-title":"Ze-HFS: Zentropy-based uncertainty measure for heterogeneous feature selection and knowledge discovery","volume":"36","author":"Yuan","year":"2024","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.patcog.2026.113972_b25","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.111303","article-title":"Relative neighborhood rough feature selection and robust classification for multi-density data","volume":"161","author":"An","year":"2025","journal-title":"Pattern Recognit."},{"issue":"6","key":"10.1016\/j.patcog.2026.113972_b26","doi-asserted-by":"crossref","DOI":"10.1007\/s10489-025-06336-1","article-title":"Feature selection based on multi-perspective dynamic neighbourhood entropy measures in a dynamic neighbourhood rough set","volume":"55","author":"Xu","year":"2025","journal-title":"Appl. Intell."},{"issue":"11","key":"10.1016\/j.patcog.2026.113972_b27","doi-asserted-by":"crossref","first-page":"3944","DOI":"10.1109\/TFUZZ.2023.3272316","article-title":"Active antinoise fuzzy dominance rough feature selection using adaptive k-nearest neighbors","volume":"31","author":"Sang","year":"2023","journal-title":"IEEE Trans. Fuzzy Syst."},{"issue":"6","key":"10.1016\/j.patcog.2026.113972_b28","doi-asserted-by":"crossref","first-page":"5559","DOI":"10.1109\/TCYB.2020.3040803","article-title":"Fast and robust attribute reduction based on the separability in fuzzy decision systems","volume":"52","author":"Hu","year":"2022","journal-title":"IEEE Trans. Cybern."},{"issue":"9","key":"10.1016\/j.patcog.2026.113972_b29","doi-asserted-by":"crossref","first-page":"5320","DOI":"10.1109\/TFUZZ.2024.3420963","article-title":"Cascaded two-stage feature clustering and selection via separability and consistency in fuzzy decision systems","volume":"32","author":"Chen","year":"2024","journal-title":"IEEE Trans. Fuzzy Syst."},{"issue":"12","key":"10.1016\/j.patcog.2026.113972_b30","doi-asserted-by":"crossref","first-page":"5698","DOI":"10.1109\/TNNLS.2020.3027351","article-title":"Robust matrix factorization with spectral embedding","volume":"32","author":"Chen","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"12","key":"10.1016\/j.patcog.2026.113972_b31","doi-asserted-by":"crossref","first-page":"7616","DOI":"10.1109\/TCYB.2024.3446764","article-title":"Robust subcluster search and mergence clustering","volume":"54","author":"Wang","year":"2024","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.patcog.2026.113972_b32","first-page":"11364","article-title":"Deep contrastive graph learning with clustering-oriented guidance","volume":"vol. 38","author":"Chen","year":"2024"},{"issue":"4","key":"10.1016\/j.patcog.2026.113972_b33","doi-asserted-by":"crossref","first-page":"1687","DOI":"10.1109\/TCYB.2025.3534195","article-title":"Generation of granular-balls for clustering based on the principle of justifiable granularity","volume":"55","author":"Jia","year":"2025","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.patcog.2026.113972_b34","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.110681","article-title":"A lie group semi-supervised FCM clustering method for image segmentation","volume":"155","author":"Sun","year":"2024","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.113972_b35","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2024.120589","article-title":"Attribute reduction with fuzzy kernel-induced relations","volume":"669","author":"Hu","year":"2024","journal-title":"Inf. Sci."},{"issue":"8","key":"10.1016\/j.patcog.2026.113972_b36","doi-asserted-by":"crossref","first-page":"2886","DOI":"10.1109\/TFUZZ.2021.3096212","article-title":"A spectral feature selection approach with kernelized fuzzy rough sets","volume":"30","author":"Chen","year":"2022","journal-title":"IEEE Trans. Fuzzy Syst."},{"issue":"11","key":"10.1016\/j.patcog.2026.113972_b37","doi-asserted-by":"crossref","first-page":"1649","DOI":"10.1109\/TKDE.2010.260","article-title":"Kernelized fuzzy rough sets and their applications","volume":"23","author":"Hu","year":"2011","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"2","key":"10.1016\/j.patcog.2026.113972_b38","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1109\/TCYB.2024.3499952","article-title":"Multi-granularity data analysis with zentropy uncertainty measure for efficient and robust feature selection","volume":"55","author":"Yuan","year":"2025","journal-title":"IEEE Trans. Cybern."},{"issue":"8","key":"10.1016\/j.patcog.2026.113972_b39","doi-asserted-by":"crossref","first-page":"7291","DOI":"10.1109\/TCYB.2021.3049684","article-title":"Locality adaptive discriminant analysis framework","volume":"52","author":"Li","year":"2022","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.patcog.2026.113972_b40","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2024.120870","article-title":"A novel multi-label feature selection method based on knowledge consistency-independence index","volume":"677","author":"Liu","year":"2024","journal-title":"Inf. Sci."},{"key":"10.1016\/j.patcog.2026.113972_b41","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2025.111853","article-title":"Locality-driven flexible consensus graph learning for multi-view clustering","volume":"169","author":"Wang","year":"2026","journal-title":"Pattern Recognit."},{"issue":"2\u20133","key":"10.1016\/j.patcog.2026.113972_b42","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/0098-3004(84)90020-7","article-title":"FCM: The fuzzy c-means clustering algorithm","volume":"10","author":"Bezdek","year":"1984","journal-title":"Comput. Geosci."},{"issue":"18","key":"10.1016\/j.patcog.2026.113972_b43","doi-asserted-by":"crossref","first-page":"3577","DOI":"10.1016\/j.ins.2008.05.024","article-title":"Neighborhood rough set based heterogeneous feature subset selection","volume":"178","author":"Hu","year":"2008","journal-title":"Inf. Sci."},{"issue":"10","key":"10.1016\/j.patcog.2026.113972_b44","doi-asserted-by":"crossref","first-page":"2009","DOI":"10.1016\/j.patcog.2004.04.007","article-title":"On cluster validity index for estimation of the optimal number of fuzzy clusters","volume":"37","author":"Kim","year":"2004","journal-title":"Pattern Recognit."},{"issue":"1","key":"10.1016\/j.patcog.2026.113972_b45","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1109\/TETCI.2022.3171784","article-title":"An emerging fuzzy feature selection method using composite entropy-based uncertainty measure and data distribution","volume":"7","author":"Xu","year":"2023","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"10.1016\/j.patcog.2026.113972_b46","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2021.107167","article-title":"A novel hybrid feature selection method considering feature interaction in neighborhood rough set","volume":"227","author":"Wan","year":"2021","journal-title":"Knowl. -Based Syst."},{"key":"10.1016\/j.patcog.2026.113972_b47","doi-asserted-by":"crossref","DOI":"10.1016\/j.fss.2024.109137","article-title":"A locally distributed rough set model for feature selection and prototype learning","volume":"498","author":"An","year":"2025","journal-title":"Fuzzy Sets and Systems"},{"key":"10.1016\/j.patcog.2026.113972_b48","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.engappai.2015.09.011","article-title":"BPSO-adaboost-KNN ensemble learning algorithm for multi-class imbalanced data classification","volume":"49","author":"Guo","year":"2016","journal-title":"Eng. Appl. Artif. Intell."}],"container-title":["Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326009374?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326009374?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T14:51:49Z","timestamp":1780930309000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0031320326009374"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":48,"alternative-id":["S0031320326009374"],"URL":"https:\/\/doi.org\/10.1016\/j.patcog.2026.113972","relation":{},"ISSN":["0031-3203"],"issn-type":[{"value":"0031-3203","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"An overlap-driven feature selection via volume-aware separability-consistency index for imbalanced classification","name":"articletitle","label":"Article Title"},{"value":"Pattern Recognition","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.patcog.2026.113972","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"113972"}}