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Conference Paper: Support Vector Machine methods for the prediction of cancer growth

TitleSupport Vector Machine methods for the prediction of cancer growth
Authors
Chen, XChing, WKAokiKinoshita, KFFuruta, K
Issue Date2010
Citation
3Rd International Joint Conference On Computational Sciences And Optimization, Cso 2010: Theoretical Development And Engineering Practice, 2010, v. 1, p. 229-232 How to Cite?
DOI: http://dx.doi.org/10.1109/CSO.2010.70
AbstractIn this paper, we study the application of Support Vector Machine (SVM) in the prediction of cancer growth. SVM is known to be an efficient method and it has been widely used for classification problems. Here we propose a classifier which can differentiate patients having different levels of cancer growth with a high classification rate. To further improve the accuracy of classification, we propose to determine the optimal size of the training set and perform feature selection using rfe-gist, a special function of SVM. © 2010 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/158875
References
References in Scopus

 

DC FieldValueLanguage
dc.contributor.authorChen, Xen_US
dc.contributor.authorChing, WKen_US
dc.contributor.authorAokiKinoshita, KFen_US
dc.contributor.authorFuruta, Ken_US
dc.date.accessioned2012-08-08T09:04:02Z-
dc.date.available2012-08-08T09:04:02Z-
dc.date.issued2010en_US
dc.identifier.citation3Rd International Joint Conference On Computational Sciences And Optimization, Cso 2010: Theoretical Development And Engineering Practice, 2010, v. 1, p. 229-232en_US
dc.identifier.urihttp://hdl.handle.net/10722/158875-
dc.description.abstractIn this paper, we study the application of Support Vector Machine (SVM) in the prediction of cancer growth. SVM is known to be an efficient method and it has been widely used for classification problems. Here we propose a classifier which can differentiate patients having different levels of cancer growth with a high classification rate. To further improve the accuracy of classification, we propose to determine the optimal size of the training set and perform feature selection using rfe-gist, a special function of SVM. © 2010 IEEE.en_US
dc.languageengen_US
dc.relation.ispartof3rd International Joint Conference on Computational Sciences and Optimization, CSO 2010: Theoretical Development and Engineering Practiceen_US
dc.titleSupport Vector Machine methods for the prediction of cancer growthen_US
dc.typeConference_Paperen_US
dc.identifier.emailChing, WK:wching@hku.hken_US
dc.identifier.authorityChing, WK=rp00679en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1109/CSO.2010.70en_US
dc.identifier.scopuseid_2-s2.0-77956442154en_US
dc.identifier.hkuros170824-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-77956442154&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume1en_US
dc.identifier.spage229en_US
dc.identifier.epage232en_US
dc.identifier.scopusauthoridChen, X=35772404700en_US
dc.identifier.scopusauthoridChing, WK=13310265500en_US
dc.identifier.scopusauthoridAokiKinoshita, KF=8704411700en_US
dc.identifier.scopusauthoridFuruta, K=7103345467en_US

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