{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T18:16:43Z","timestamp":1780510603491,"version":"3.54.1"},"reference-count":21,"publisher":"IEEE","license":[{"start":{"date-parts":[[2019,7,1]],"date-time":"2019-07-01T00:00:00Z","timestamp":1561939200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2019,7,1]],"date-time":"2019-07-01T00:00:00Z","timestamp":1561939200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2019,7,1]],"date-time":"2019-07-01T00:00:00Z","timestamp":1561939200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,7]]},"DOI":"10.1109\/ijcnn.2019.8852097","type":"proceedings-article","created":{"date-parts":[[2019,10,1]],"date-time":"2019-10-01T03:44:32Z","timestamp":1569901472000},"page":"1-8","source":"Crossref","is-referenced-by-count":9,"title":["GMM-VRD: A Gaussian Mixture Model for Dealing With Virtual and Real Concept Drifts"],"prefix":"10.1109","author":[{"given":"Gustavo H. F. M.","family":"Oliveira","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leandro L.","family":"Minku","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adriano L. I.","family":"Oliveira","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2011.6033578"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2018.03.021"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/79.543975"},{"key":"ref13","article-title":"An ensemble generation methodbased on instance hardness","author":"walmsley","year":"2018"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2011.08.019"},{"key":"ref15","first-page":"239","article-title":"Time series forecasting in the presence of concept drift: A pso-based approach","author":"oliveira","year":"2017","journal-title":"ICTAI 2017 IEEE 29th International Conference on"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-16138-4_9"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-018-5719-z"},{"key":"ref18","article-title":"Real datasets with concept drift","year":"0"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2016.2526675"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2014.07.019"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2011.58"},{"key":"ref6","doi-asserted-by":"crossref","first-page":"1078","DOI":"10.1109\/34.879789","article-title":"Ica mixture models for unsupervised classification of non-gaussian classes and automatic context switching in blind signal separation","volume":"22","author":"lee","year":"2000","journal-title":"IEEE TPAMI"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/s12530-016-9168-2"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.compchemeng.2003.09.031"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/BRACIS.2015.61"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/2523813"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/MCI.2015.2471196"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.compchemeng.2009.08.007"},{"key":"ref20","first-page":"1","article-title":"Statistical comparisons of classifiers over multiple data sets","volume":"7","author":"dem\u0161ar","year":"2006","journal-title":"Journal of Machine Learning Research"},{"key":"ref21","article-title":"How good is the electricity benchmark for evaluating concept drift adaptation","author":"zliobaite","year":"2013"}],"event":{"name":"2019 International Joint Conference on Neural Networks (IJCNN)","location":"Budapest, Hungary","start":{"date-parts":[[2019,7,14]]},"end":{"date-parts":[[2019,7,19]]}},"container-title":["2019 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8840768\/8851681\/08852097.pdf?arnumber=8852097","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,17]],"date-time":"2022-07-17T21:51:28Z","timestamp":1658094688000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8852097\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7]]},"references-count":21,"URL":"https:\/\/doi.org\/10.1109\/ijcnn.2019.8852097","relation":{},"subject":[],"published":{"date-parts":[[2019,7]]}}}