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dc.contributor.authorZhu, Lei
dc.description.abstractAbstract Linear Proximal Support Vector Machines (LPSVM), 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 an incremental LPSVM ter-med DCIL-IncLPSVM that has robust learning performance under class imbalance. In doing so, we simplify a weighted LPSVM, which is computationally not renewable, as several core matrices multiplying two simple weight coefficients. When data addition and/or retirement occurs, the proposed DCIL-IncLPSVM accommodates current 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 batch SVM and LPSVM in terms of F-measure, relative sensitivity 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 classic incremental SVM (IncSVM) and incremental LPSVM (IncLPSVM).
dc.rightsAll rights reserveden_NZ
dc.subjectabstract linear proximal support vector machinesen_NZ
dc.subjectclass imbalance learningen_NZ
dc.subjectdata miningen_NZ
dc.titleDynamic class imbalance learning for incremental LPSVMen_NZ
dc.typeMasters Thesisen_NZ
dc.rights.holderAuthoren_NZ of Computingen_NZ Institute of Technologyen_NZ
dc.subject.marsden170203 Knowledge Representation and Machine Learningen_NZ
dc.identifier.bibliographicCitationZhu, L. (2012). Dynamic class imbalance learning for incremental LPSVM. (Unpublished document submitted in partial fulfilment of the requirements for the degree of Master of Computing). Unitec Institute of Technology. Retrieved from
dc.contributor.affiliationUnitec Institute of Technologyen_NZ
unitec.advisor.principalPang, Paul
unitec.advisor.associatedChen, Aaron

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