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dc.contributor.authorNguyen, Tien Thanhen_US
dc.contributor.authorPham, Xuanen_US
dc.contributor.authorLiew, Wee-Chungen_US
dc.contributor.authorPedrycz, Witolden_US
dc.date.accessioned2019-06-08T01:41:52Z
dc.date.available2019-06-08T01:41:52Z
dc.date.issued2018en_US
dc.identifier.issn2168-2267en_US
dc.identifier.doi10.1109/TCYB.2018.2821679en_US
dc.identifier.urihttp://hdl.handle.net/10072/381767
dc.description.abstractIn this paper, we introduced a new approach of combining multiple classifiers in a heterogeneous ensemble system. Instead of using numerical membership values when combining, we constructed interval membership values for each class prediction from the meta-data of observation by using the concept of information granule. In the proposed method, the uncertainty (diversity) of the predictions produced by the base classifiers is quantified by the interval-based information granules. The decision model is then generated by considering both bound and length of the intervals. Extensive experimentation using the UCI datasets has demonstrated the superior performance of our algorithm over other algorithms including six fixed combining methods, one trainable combining method, AdaBoost, bagging, and random subspace.en_US
dc.description.peerreviewedYesen_US
dc.languageEnglishen_US
dc.publisherIEEEen_US
dc.publisher.placeUnited Statesen_US
dc.relation.ispartofpagefrom1en_US
dc.relation.ispartofpageto10en_US
dc.relation.ispartofjournalIEEE Transactions on Cyberneticsen_US
dc.subject.fieldofresearchElectrical and Electronic Engineering not elsewhere classifieden_US
dc.subject.fieldofresearchcode090699en_US
dc.titleAggregation of Classifiers: A Justifiable Information Granularity Approachen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Articlesen_US
dc.type.codeC - Journal Articlesen_US
gro.facultyGriffith Sciences, School of Information and Communication Technologyen_US
gro.description.notepublicThis publication has been entered into Griffith Research Online as an Advanced Online Version.en_US
gro.rights.copyright© 2018 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.en_US
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