Show simple item record

dc.contributor.authorNguyen, TT
dc.contributor.authorLiew, AWC
dc.contributor.authorTran, MT
dc.contributor.authorNguyen, MP
dc.contributor.editorX. Wang, W. Pedrycz, P. Chan, Q. He
dc.date.accessioned2017-10-20T02:53:30Z
dc.date.available2017-10-20T02:53:30Z
dc.date.issued2014
dc.identifier.isbn9783662456514
dc.identifier.issn1865-0929
dc.identifier.refurihttp://www.icmlc.com/ICMLC/formerICMLC_2014.html
dc.identifier.doi10.1007/978-3-662-45652-1_1
dc.identifier.urihttp://hdl.handle.net/10072/67141
dc.description.abstractCombining multiple classifiers to achieve better performance than any single classifier is one of the most important research areas in machine learning. In this paper, we focus on combining different classifiers to form an effective ensemble system. By introducing a novel framework operated on outputs of different classifiers, our aim is to build a powerful model which is competitive to other well-known combining algorithms such as Decision Template, Multiple Response Linear Regression (MLR), SCANN and fixed combining rules. Our approach is difference from the traditional approaches in that we use Gaussian Mixture Model (GMM) to model distribution of Level1 data and to predict the label of an observation based on maximizing the posterior probability realized through Bayes model. We also apply Principle Component Analysis (PCA) to output of base classifiers to reduce its dimension of what before GMM modeling. Experiments were evaluated on 21 datasets coming from University of California Irvine (UCI) Machine Learning Repository to demonstrate the benefits of our framework compared with several benchmark algorithms.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.languageEnglish
dc.publisherSpringer
dc.publisher.placeGermany
dc.publisher.urihttp://www.icmlc.com/ICMLC/formerICMLC_2014.html
dc.relation.ispartofstudentpublicationY
dc.relation.ispartofconferencenameICMLC 2014
dc.relation.ispartofconferencetitleCommunications in Computer and Information Science
dc.relation.ispartofdatefrom2014-07-13
dc.relation.ispartofdateto2014-07-16
dc.relation.ispartoflocationLanzhou, China
dc.relation.ispartofpagefrom3
dc.relation.ispartofpageto12
dc.relation.ispartofvolume481
dc.rights.retentionY
dc.subject.fieldofresearchExpert Systems
dc.subject.fieldofresearchcode080105
dc.titleCombining Classifiers Based on Gaussian Mixture Model Approach to Ensemble Data
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
dc.description.versionAccepted Manuscript (AM)
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.rights.copyright© 2014 Springer Berlin/Heidelberg. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. The original publication is available at www.springerlink.com
gro.hasfulltextFull Text
gro.griffith.authorLiew, Alan Wee-Chung


Files in this item

This item appears in the following Collection(s)

  • Conference outputs
    Contains papers delivered by Griffith authors at national and international conferences.

Show simple item record