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dc.contributor.authorTien, Thanh Nguyen
dc.contributor.authorAlan, Wee-Chung Liew
dc.contributor.authorMinh, Toan Tran
dc.contributor.authorMai, Phuong Nguyen
dc.contributor.editorHuang, DS
dc.contributor.editorJo, KH
dc.contributor.editorWang, L
dc.date.accessioned2017-05-03T15:20:18Z
dc.date.available2017-05-03T15:20:18Z
dc.date.issued2014
dc.identifier.isbn978-3-319-09338-3
dc.identifier.issn0302-9743
dc.identifier.refurihttp://ic-ic.tongji.edu.cn/2014/index.htm
dc.identifier.doi10.1007/978-3-319-09339-0_6
dc.identifier.urihttp://hdl.handle.net/10072/65367
dc.description.abstractCombining outputs from different classifiers to achieve high accuracy in classification task is one of the most active research areas in ensemble method. Although many state-of-art approaches have been introduced, no one method performs the best on all data sources. With the aim of introducing an effective classification model, we propose a Gaussian Mixture Model (GMM) based method that combines outputs of base classifiers (called meta-data or Level1 data) resulted from Stacking Algorithm. We further apply Genetic Algorithm (GA) to that data as a feature selection strategy to explore an optimal subset of Level1 data in which our GMM-based approach can achieve high accuracy. Experiments on 21 UCI Machine Learning Repository data files and CLEF2009 medical image database demonstrate the advantage of our framework compared with other well-known combining algorithms such as Decision Template, Multiple Response Linear Regression (MLR), SCANN and fixed combining rules as well as GMM-based approaches on original data.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent289810 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoen_US
dc.publisherSpringer
dc.publisher.placeChina
dc.publisher.urihttp://ic-ic.tongji.edu.cn/2014/index.htm
dc.relation.ispartofstudentpublicationY
dc.relation.ispartofconferencename10th International Conference on Intelligent Computing (ICIC)
dc.relation.ispartofconferencetitleINTELLIGENT COMPUTING METHODOLOGIES
dc.relation.ispartofdatefrom2014-08-03
dc.relation.ispartofdateto2014-08-06
dc.relation.ispartoflocationTaiyuan, PEOPLES R CHINA
dc.relation.ispartofpagefrom56
dc.relation.ispartofpagefrom12 pages
dc.relation.ispartofpageto67
dc.relation.ispartofpageto12 pages
dc.relation.ispartofvolume8589
dc.rights.retentionY
dc.subject.fieldofresearchExpert Systems
dc.subject.fieldofresearchcode080105
dc.titleCombining Multi Classifiers based on A Genetic Algorithm: A Gaussian Mixture Model framework
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.rights.copyright© 2014 Springer International Publishing Switzerland. 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.date.issued2015-09-16T05:40:46Z
gro.hasfulltextFull Text
gro.griffith.authorLiew, Alan Wee-Chung
gro.griffith.authorNguyen, Tien Thanh T.


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