Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/36721
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dc.contributor.authorSun, Y.-
dc.contributor.authorTang, K.-
dc.contributor.authorMinku, Leandro Lei-
dc.contributor.authorWang, S.-
dc.contributor.authorYao, X.-
dc.date.accessioned2016-02-16T11:35:26Z-
dc.date.available2016-02-16T11:35:26Z-
dc.date.issued2016-02-08-
dc.identifier.citationIEEE Transactions on Knowledge and Data Engineering, 2016, 28 (6), pp. 1532-1545en
dc.identifier.issn1041-4347-
dc.identifier.urihttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7401075en
dc.identifier.urihttp://hdl.handle.net/2381/36721-
dc.description.abstractClass evolution, the phenomenon of class emergence and disappearance, is an important research topic for data stream mining. All previous studies implicitly regard class evolution as a transient change, which is not true for many real-world problems. This paper concerns the scenario where classes emerge or disappear gradually. A class-based ensemble approach, namely Class-Based ensemble for Class Evolution (CBCE), is proposed. By maintaining a base learner for each class and dynamically updating the base learners with new data, CBCE can rapidly adjust to class evolution. A novel under-sampling method for the base learners is also proposed to handle the dynamic class-imbalance problem caused by the gradual evolution of classes. Empirical studies demonstrate the effectiveness of CBCE in various class evolution scenarios in comparison to existing class evolution adaptation methods.en
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE), United Statesen
dc.rightsCopyright © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Deposited with reference to the publisher’s archiving policy available on the SHERPA/RoMEO website.en
dc.subjectAdaptation modelsen
dc.subjectComputer scienceen
dc.subjectData mininGen
dc.subjectData modelsen
dc.subjectProbability distributionen
dc.subjectSunen
dc.subjectTransient analysisen
dc.subjectclass evolutionen
dc.subjectdata stream miningen
dc.subjectensemble modelen
dc.subjectimbalanced classificationen
dc.subjecton-line learningen
dc.titleOnline Ensemble Learning of Data Streams with Gradually Evolved Classesen
dc.typeJournal Articleen
dc.identifier.doi10.1109/TKDE.2016.2526675-
dc.description.statusPeer-revieweden
dc.description.versionPost-printen
dc.type.subtypeArticle-
pubs.organisational-group/Organisationen
pubs.organisational-group/Organisation/COLLEGE OF SCIENCE AND ENGINEERINGen
pubs.organisational-group/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Computer Scienceen
dc.dateaccepted2016-01-27-
Appears in Collections:Published Articles, Dept. of Computer Science

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