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dc.contributor.authorTien, Thanh Nguyen
dc.contributor.authorMai, Phuong Nguyen
dc.contributor.authorXuan, Cuong Pham
dc.contributor.authorLiew, Alan Wee-Chung
dc.contributor.authorPedrycz, Witold
dc.date.accessioned2019-07-04T12:41:44Z
dc.date.available2019-07-04T12:41:44Z
dc.date.issued2018
dc.identifier.issn1568-4946
dc.identifier.doi10.1016/j.asoc.2018.09.021
dc.identifier.urihttp://hdl.handle.net/10072/381986
dc.description.abstractIn this study, a novel framework to combine multiple classifiers in an ensemble system is introduced. Here we exploit the concept of information granule to construct granular prototypes for each class on the outputs of an ensemble of base classifiers. In the proposed method, uncertainty in the outputs of the base classifiers on training observations is captured by an interval-based representation. To predict the class label for a new observation, we first determine the distances between the output of the base classifiers for this observation and the class prototypes, then the predicted class label is obtained by choosing the label associated with the shortest distance. In the experimental study, we combine several learning algorithms to build the ensemble system and conduct experiments on the UCI, colon cancer, and selected CLEF2009 datasets. The experimental results demonstrate that the proposed framework outperforms several benchmarked algorithms including two trainable combining methods, i.e., Decision Template and Two Stages Ensemble System, AdaBoost, Random Forest, L2-loss Linear Support Vector Machine, and Decision Tree.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier
dc.publisher.placeNetherlands
dc.relation.ispartofpagefrom795
dc.relation.ispartofpageto815
dc.relation.ispartofjournalApplied Soft Computing
dc.relation.ispartofvolume73
dc.subject.fieldofresearchInformation Systems not elsewhere classified
dc.subject.fieldofresearchApplied Mathematics
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchInformation Systems
dc.subject.fieldofresearchcode080699
dc.subject.fieldofresearchcode0102
dc.subject.fieldofresearchcode0801
dc.subject.fieldofresearchcode0806
dc.subject.keywordsEnsemble method
dc.subject.keywordsMultiple classifiers system
dc.subject.keywordsInformation granule
dc.subject.keywordsInformation uncertainty
dc.subject.keywordsSupervised learning
dc.titleCombining heterogeneous classifiers via granular prototypes
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.hasfulltextNo Full Text
gro.griffith.authorLiew, Alan Wee-Chung
gro.griffith.authorPham, Cuong X.


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