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dc.contributor.authorPang, Shaoning
dc.contributor.authorZhu, Lei
dc.contributor.authorChen, Gang
dc.contributor.authorSarrafzadeh, Hossein
dc.contributor.authorBan, Tao
dc.contributor.authorInoue, Daisuke
dc.date.accessioned2014-03-20T22:14:10Z
dc.date.available2014-03-20T22:14:10Z
dc.date.issued2013-02
dc.identifier.urihttps://hdl.handle.net/10652/2383
dc.description.abstractLinear Proximal Support Vector Machines (LPSVMs), like decision trees, classic SVM, etc. are originally not equipped to handle drifting data streams that exhibit high and varying degrees of class imbalance. For online classification of data streams with imbalanced class distribution, we propose a dynamic class imbalance learning (DCIL) approach to incremental LPSVM (IncLPSVM) modeling. In doing so, we simplify a computationally non-renewable weighted LPSVM to several core matrices multiplying two simple weight coefficients. When data addition and/or retirement occurs, the proposed DCIL-IncLPSVM1 accommodates newly presented class imbalance by a simple matrix and coefficient updating, meanwhile ensures no discriminative information lost throughout the learning process. Experiments on benchmark datasets indicate that the proposed DCIL-IncLPSVM outperforms classic IncSVM and IncLPSVM in terms of F-measure and G-mean metrics. Moreover, our application to online face membership authentication shows that the proposed DCIL-IncLPSVM remains effective in the presence of highly dynamic class imbalance, which usually poses serious problems to previous approaches.en_NZ
dc.language.isoenen_NZ
dc.publisherNeural Networksen_NZ
dc.subjectabstract linear proximal support vector machinesen_NZ
dc.titleDynamic class imbalance learning for incremental LPSVMen_NZ
dc.typeJournal Articleen_NZ
dc.rights.holderNeural Networksen_NZ
dc.subject.marsden170203 Knowledge Representation and Machine Learningen_NZ
dc.identifier.bibliographicCitationPang, S., Zhu, L., Chen, G., Sarrafzadeh, A., Ban, T., and Inoue, D. (2013). Dynamic class imbalance learning for incremental LPSVM. Neural Networks. 44. 87–100.en_NZ
unitec.institutionUnitec Institute of Technologyen_NZ
unitec.institutionNational Institute of Information and Communications Technology (Tokyo, Japan)en_NZ
unitec.publication.spage87en_NZ
unitec.publication.lpage100en_NZ
unitec.publication.volume44en_NZ
unitec.publication.titleNeural Networksen_NZ
unitec.peerreviewedyesen_NZ


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