{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:27:06Z","timestamp":1772119626848,"version":"3.50.1"},"reference-count":42,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"6","license":[{"start":{"date-parts":[[2015,6,1]],"date-time":"2015-06-01T00:00:00Z","timestamp":1433116800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61173123"],"award-info":[{"award-number":["61173123"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation Project of Zhejiang Province","award":["LR13F030003"],"award-info":[{"award-number":["LR13F030003"]}]},{"name":"Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China","award":["ICT1315"],"award-info":[{"award-number":["ICT1315"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Cybern."],"published-print":{"date-parts":[[2015,6]]},"DOI":"10.1109\/tcyb.2014.2347372","type":"journal-article","created":{"date-parts":[[2014,9,26]],"date-time":"2014-09-26T19:21:14Z","timestamp":1411759274000},"page":"1209-1221","source":"Crossref","is-referenced-by-count":93,"title":["Feature Selection Based on Dependency Margin"],"prefix":"10.1109","volume":"45","author":[{"given":"Yong","family":"Liu","sequence":"first","affiliation":[]},{"given":"Feng","family":"Tang","sequence":"additional","affiliation":[]},{"given":"Zhiyong","family":"Zeng","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","author":"witten","year":"2011","journal-title":"Data Mining Practical Machine Learning Tools and Techniques"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1023\/A:1025667309714"},{"key":"ref33","first-page":"74","article-title":"A Bayesian view of challenges in feature selection: Feature aggregation, multiple targets, redundancy and interaction","volume":"4","author":"antal","year":"2008","journal-title":"J Machine Learning Research Workshop and Conf Proc"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CSB.2003.1227396"},{"key":"ref31","first-page":"242","article-title":"Feature selection for high-dimensional data&#x2014;A Pearson redundancy based filter","author":"biesiada","year":"2008","journal-title":"Computer Recognition Systems 2"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2010.2044189"},{"key":"ref37","author":"pearl","year":"1988","journal-title":"Probabilistic Reasoning in Intelligent Systems Networks of Plausible Inference"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/ICAPR.2009.36"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/1014052.1014149"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/B978-1-55860-335-6.50023-4"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2004.71"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2007.1093"},{"key":"ref11","doi-asserted-by":"crossref","first-page":"2013","DOI":"10.1109\/TPAMI.2011.44","article-title":"A variance minimization criterion to feature selection using Laplacian regularization","volume":"33","author":"he","year":"2011","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2007.06.014"},{"key":"ref13","first-page":"1855","article-title":"Feature selection for unsupervised and supervised inference: The emergence of sparsity in a weight-based approach","volume":"6","author":"wolf","year":"2005","journal-title":"J Mach Learn Res"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2005.66"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/S0004-3702(97)00043-X"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.3115\/1075527.1075574"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2005.159"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/S0004-3702(03)00079-1"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2008.05.024"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2010.263"},{"key":"ref4","first-page":"284","article-title":"Toward optimal feature selection","author":"koller","year":"1996","journal-title":"Proc 13th Int Conf Machine Learning (ICML)"},{"key":"ref27","first-page":"1289","article-title":"An extensive empirical study of feature selection metrics for text classification","volume":"3","author":"forman","year":"2003","journal-title":"J Mach Learn Res"},{"key":"ref3","first-page":"1205","article-title":"Efficient feature selection via analysis of relevance and redundancy","volume":"5","author":"yu","year":"2004","journal-title":"J Mach Learn Res"},{"key":"ref6","doi-asserted-by":"crossref","first-page":"1517","DOI":"10.1109\/TPAMI.2010.84","article-title":"On the complexity of discrete feature selection for optimal classification","volume":"32","author":"pe\u00f1a","year":"2010","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/1015330.1015397"},{"key":"ref5","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1109\/TSMCB.2009.2024166","article-title":"Selecting discrete and continuous features based on neighborhood decision error minimization","volume":"40","author":"hu","year":"2010","journal-title":"IEEE Trans Syst Man Cybern B Cybern"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCB.2012.2227469"},{"key":"ref7","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1109\/TCYB.2013.2269765","article-title":"Extremely high-dimensional feature selection via feature generating samplings","volume":"44","author":"li","year":"2014","journal-title":"IEEE Trans Cybern"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2013.2281820"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2013.2272642"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2005.105"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2003.1233904"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/B978-1-55860-247-2.50050-4"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCB.2012.2225832"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2002.1017623"},{"key":"ref24","first-page":"1157","article-title":"An introduction to variable and feature selection","volume":"3","author":"guyon","year":"2003","journal-title":"J Mach Learn Res"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2006.244"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/1277741.1277811"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/1014052.1014149"},{"key":"ref25","first-page":"359","article-title":"Correlation-based feature selection for discrete and numeric class machine learning","author":"hall","year":"2000","journal-title":"Proc 17th Int Conf Mach Learn (ICML)"}],"container-title":["IEEE Transactions on Cybernetics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6221036\/7106596\/06912011.pdf?arnumber=6912011","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T15:58:46Z","timestamp":1642003126000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/6912011\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,6]]},"references-count":42,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.1109\/tcyb.2014.2347372","relation":{},"ISSN":["2168-2267","2168-2275"],"issn-type":[{"value":"2168-2267","type":"print"},{"value":"2168-2275","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,6]]}}}