title: Dynamic class imbalance learning for incremental LPSVM.
abstract: Linear 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-IncLPSVM(1) 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.
authors.affiliation: Department of Computing, Unitec Institute of Technology, Private Bag 92025, Auckland 1025, New Zealand. ppang@unitec.ac.nz
authors.forename: Shaoning
authors.initials: S
authors.lastname: Pang
authors.forename: Lei
authors.initials: L
authors.lastname: Zhu
authors.forename: Gang
authors.initials: G
authors.lastname: Chen
authors.forename: Abdolhossein
authors.initials: A
authors.lastname: Sarrafzadeh
authors.forename: Tao
authors.initials: T
authors.lastname: Ban
authors.forename: Daisuke
authors.initials: D
authors.lastname: Inoue
mesh_terms: D001185:Artificial Intelligence; D060388:Support Vector Machine