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dc.contributor.authorTien, Thanh Nguyen
dc.contributor.authorXuan, Cuong Pham
dc.contributor.authorLiew, Alan Wee-Chung
dc.contributor.authorPedrycz, Witold
dc.date.accessioned2019-06-08T01:41:52Z
dc.date.available2019-06-08T01:41:52Z
dc.date.issued2019
dc.identifier.issn2168-2267
dc.identifier.doi10.1109/TCYB.2018.2821679
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.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherIEEE
dc.publisher.placeUnited States
dc.relation.ispartofpagefrom1
dc.relation.ispartofpageto10
dc.relation.ispartofjournalIEEE Transactions on Cybernetics
dc.subject.fieldofresearchElectrical and Electronic Engineering not elsewhere classified
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchApplied Mathematics
dc.subject.fieldofresearchElectrical and Electronic Engineering
dc.subject.fieldofresearchcode090699
dc.subject.fieldofresearchcode0801
dc.subject.fieldofresearchcode0102
dc.subject.fieldofresearchcode0906
dc.titleAggregation of Classifiers: A Justifiable Information Granularity Approach
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.description.notepublicThis publication has been entered into Griffith Research Online as an Advanced Online Version.
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.
gro.hasfulltextFull Text
gro.griffith.authorLiew, Alan Wee-Chung
gro.griffith.authorNguyen, Tien Thanh T.


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