Dynamic class imbalance learning for incremental LPSVM
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Authors
Pang, Shaoning
Zhu, Lei
Chen, Gang
Sarrafzadeh, Hossein
Ban, Tao
Inoue, Daisuke
Zhu, Lei
Chen, Gang
Sarrafzadeh, Hossein
Ban, Tao
Inoue, Daisuke
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Grantor
Date
2013-02
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Type
Journal Article
Ngā Upoko Tukutuku (Māori subject headings)
Keyword
abstract linear proximal support vector machines
ANZSRC Field of Research Code (2020)
Citation
Pang, 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.
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-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.
Publisher
Neural Networks
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Neural Networks
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