{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T19:53:54Z","timestamp":1780084434605,"version":"3.54.0"},"reference-count":57,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100002367","name":"Science and Technology Service Network Program of Chinese Academy of Sciences","doi-asserted-by":"publisher","award":["KFJ-STS-ZDTP-060"],"award-info":[{"award-number":["KFJ-STS-ZDTP-060"]}],"id":[{"id":"10.13039\/501100002367","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["71731009"],"award-info":[{"award-number":["71731009"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61472390"],"award-info":[{"award-number":["61472390"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Beijing Social Science Foundation Project","award":["17GLB020"],"award-info":[{"award-number":["17GLB020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/access.2020.3015954","type":"journal-article","created":{"date-parts":[[2020,8,12]],"date-time":"2020-08-12T20:42:42Z","timestamp":1597264962000},"page":"149899-149914","source":"Crossref","is-referenced-by-count":26,"title":["Incremental Cost-Sensitive Support Vector Machine With Linear-Exponential Loss"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3725-0428","authenticated-orcid":false,"given":"Yue","family":"Ma","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kun","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4675-0398","authenticated-orcid":false,"given":"Yingjie","family":"Tian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-019-01033-1"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2017.09.013"},{"key":"ref33","article-title":"Classification of imbalance data using tomek link (T-link) combined with random under-sampling (RUS) as a data reduction method","author":"elhassan","year":"2016"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/1321440.1321461"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2018.06.056"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-011-0465-6"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2016.11.014"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-009-0198-y"},{"key":"ref35","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1109\/TSMCB.2008.2007853","article-title":"Exploratory undersampling for class-imbalance learning","volume":"39","author":"liu","year":"2009","journal-title":"IEEE Trans Syst Man Cybern B Cybern"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ICECCT.2019.8869339"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/11538059_91"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-30115-8_7"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2012.232"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.12.036"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.32604\/cmc.2019.03708"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2009.07.006"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2015.03.013"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2014.2342533"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2005.56"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-018-1772-4"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1613\/jair.953"},{"key":"ref25","first-page":"759","article-title":"Risk minimization, probability elicitation, and cost-sensitive SVMs","author":"masnadi-shirazi","year":"2010","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1162\/NECO_a_00155"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2016.09.045"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2003.1223991"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1145\/1961189.1961199"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-010-9166-x"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2007.12.031"},{"key":"ref53","author":"lichman","year":"2013","journal-title":"UCI Machine Learning Repository"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511804441"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2013.02.007"},{"key":"ref11","article-title":"Cost-sensitive learning with asymmetric loss function","author":"ma","year":"0","journal-title":"Neurocomputing"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2017.07.006"},{"key":"ref12","first-page":"65","article-title":"Adaptive online gradient descent","author":"hazan","year":"2008","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1561\/2200000018"},{"key":"ref14","first-page":"567","article-title":"Stochastic dual coordinate ascent methods for regularized loss minimization","volume":"14","author":"shalev-shwartz","year":"2013","journal-title":"J Mach Learn Res"},{"key":"ref15","first-page":"3103","article-title":"Breaking the curse of kernelization: Budgeted stochastic gradient descent for large-scale SVM training","volume":"13","author":"wang","year":"2012","journal-title":"J Mach Learn Res"},{"key":"ref16","article-title":"Block-coordinate Frank–Wolfe optimization for structural SVMs","author":"lacoste-julien","year":"2012","journal-title":"arXiv 1207 4747"},{"key":"ref17","article-title":"Online alternating direction method (longer version)","author":"wang","year":"2013","journal-title":"arXiv 1306 3721"},{"key":"ref18","first-page":"392","article-title":"Dual averaging and proximal gradient descent for online alternating direction multiplier method","author":"suzuki","year":"2013","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2010.2048039"},{"key":"ref4","volume":"1","author":"vapnik","year":"1998","journal-title":"Statistical Learning Theory"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/BF00994018"},{"key":"ref6","first-page":"409","article-title":"Incremental and decremental support vector machine learning","author":"cauwenberghs","year":"2001","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/s10107-010-0420-4"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2018.04.049"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2018.12.003"},{"key":"ref49","doi-asserted-by":"crossref","first-page":"1561","DOI":"10.1109\/TPAMI.2010.220","article-title":"Maximum correntropy criterion for robust face recognition","volume":"33","author":"he","year":"2011","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2018.05.023"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/34.120331"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2018.02.051"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1137\/030600862"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/83.392335"},{"key":"ref42","article-title":"Support vector machines: Training and applications","author":"osuna","year":"1998"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2894148"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2011.01.061"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2019.02.062"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8948470\/09165545.pdf?arnumber=9165545","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T15:56:47Z","timestamp":1642003007000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9165545\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":57,"URL":"https:\/\/doi.org\/10.1109\/access.2020.3015954","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}