{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T05:02:41Z","timestamp":1764997361032,"version":"3.37.3"},"reference-count":40,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61403397"],"award-info":[{"award-number":["61403397"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Basic Research Plan in Shaanxi Province of China","award":["2020JM-358","2015JM6313"],"award-info":[{"award-number":["2020JM-358","2015JM6313"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/access.2020.3046604","type":"journal-article","created":{"date-parts":[[2020,12,23]],"date-time":"2020-12-23T20:39:16Z","timestamp":1608755956000},"page":"565-580","source":"Crossref","is-referenced-by-count":7,"title":["Multiple Kernel Learning With Minority Oversampling for Classifying Imbalanced Data"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2565-7095","authenticated-orcid":false,"given":"Ling","family":"Wang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1887-0891","authenticated-orcid":false,"given":"Hongqiao","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1960-0546","authenticated-orcid":false,"given":"Guangyuan","family":"Fu","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","article-title":"Keel data-mining software tool: Data set repository, integration of algorithms and experimental analysis framework","volume":"17","author":"alcal\u00e1-fdez","year":"2011","journal-title":"J Multiple-Valued Logic Soft Comput"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2013.12.003"},{"key":"ref33","first-page":"27","article-title":"Learning the kernel matrix with semidefinite programming","volume":"5","author":"lanckriet","year":"2004","journal-title":"J Mach Learn Res"},{"key":"ref32","doi-asserted-by":"crossref","first-page":"1354","DOI":"10.1109\/TPAMI.2013.212","article-title":"Multiple kernel learning for visual object recognition: A review","volume":"36","author":"bucak","year":"2014","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bth294"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2017.2729882"},{"journal-title":"The Nature of Statistical Learning Theory","year":"2013","author":"vapnik","key":"ref37"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1051\/ro\/197408V300731"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1137\/S1052623494267127"},{"key":"ref34","first-page":"2491","article-title":"SimpleMKL","volume":"9","author":"rakotomamonjy","year":"2008","journal-title":"J Mach Learn Res"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1613\/jair.1.11192"},{"key":"ref11","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1007\/978-3-642-13529-3_18","article-title":"Learning from imbalanced data in presence of noisy and borderline examples","author":"napiera?a","year":"2010","journal-title":"Proc Int Conf Rough Sets Current Trends Comput"},{"key":"ref40","first-page":"1","article-title":"Statistical comparisons of classifiers over multiple data sets","volume":"7","author":"dem\u0161ar","year":"2006","journal-title":"J Mach Learn Res"},{"key":"ref12","first-page":"878","article-title":"Borderline-smote: A new over-sampling method in imbalanced data sets learning","author":"han","year":"2005","journal-title":"Proc Int Conf Intell Comput"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2016.10.041"},{"key":"ref14","first-page":"1322","article-title":"ADASYN: Adaptive synthetic sampling approach for imbalanced learning","author":"he","year":"2008","journal-title":"Proc IEEE Int Joint Conf Neural Netw"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1007\/978-3-642-01307-2_43","article-title":"Safe-level-smote: Safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem","author":"bunkhumpornpat","year":"2009","journal-title":"Proc Pacific–Asia Conf Knowl Discovery Data Mining"},{"key":"ref16","article-title":"A novel density-based adaptive k nearest neighbor method for dealing with overlapping problem in imbalanced datasets","author":"yuan","year":"2020","journal-title":"Neural Comput Appl"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/IECON.2015.7392251"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2017.2751612"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.06.065"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/s13748-016-0094-0"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.10.042"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2008.239"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/aab2f2"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2890693"},{"key":"ref29","first-page":"2211","article-title":"Multiple kernel learning algorithms","volume":"12","author":"g\u00f6nen","year":"2011","journal-title":"J Mach Learn Res"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/s11517-016-1482-0"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2010.5596787"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/1007730.1007734"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/LSENS.2018.2879990"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1613\/jair.953"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2914680"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2010.2042721"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2016.09.032"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2019.2898371"},{"key":"ref24","first-page":"97","article-title":"AdaCost: Misclassification cost-sensitive boosting","volume":"99","author":"fan","year":"1999","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCC.2011.2161285"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2007.04.009"},{"key":"ref25","first-page":"983","article-title":"A comparative study of cost-sensitive boosting algorithms","author":"ting","year":"2000","journal-title":"Proc 17th Int Conf Mach Learn"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9312710\/09305259.pdf?arnumber=9305259","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,25]],"date-time":"2022-01-25T20:58:50Z","timestamp":1643144330000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9305259\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":40,"URL":"https:\/\/doi.org\/10.1109\/access.2020.3046604","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2021]]}}}