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dc.contributor.authorTien, Thanh Nguyen
dc.contributor.authorThi, Thu Thuy Nguyen
dc.contributor.authorXuan, Cuong Pham
dc.contributor.authorLiew, Alan Wee-Chung
dc.date.accessioned2017-11-30T03:08:56Z
dc.date.available2017-11-30T03:08:56Z
dc.date.issued2016
dc.identifier.issn0031-3203
dc.identifier.doi10.1016/j.patcog.2015.06.016
dc.identifier.urihttp://hdl.handle.net/10072/142514
dc.description.abstractIn this paper, we propose a combining classifier method based on the Bayesian inference framework. Specifically, the outputs of base classifiers (called Level1 data or meta-data) are utilized in a combiner to produce the final classification. In our ensemble system, each class in the training set induces a distribution on the Level1 data, which is modeled by a multivariate Gaussian distribution. Traditionally, the parameters of the Gaussian are estimated using a maximum likelihood approach. However, maximum likelihood estimation cannot be applied since the covariance matrix of Level1 data of each class is not full rank. Instead, we propose to estimate the multivariate Gaussian distribution of Level1 data of each class by using the Variational Inference method. Experiments conducted on eighteen UCI Machine Learning Repository datasets and a selected 10-class CLEF2009 medical imaging database demonstrated the advantage of our method compared with several well-known ensemble methods.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofpagefrom198
dc.relation.ispartofpageto212
dc.relation.ispartofjournalPattern Recognition
dc.relation.ispartofvolume49
dc.subject.fieldofresearchArtificial intelligence
dc.subject.fieldofresearchcode4602
dc.titleA novel combining classifier method based on Variational Inference
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.hasfulltextNo Full Text
gro.griffith.authorLiew, Alan Wee-Chung


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