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    Dynamic class imbalance learning for incremental LPSVM

    Pang, Shaoning; Zhu, Lei; Chen, Gang; Sarrafzadeh, Hossein; Ban, Tao; Inoue, Daisuke

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    Date
    2013-02
    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.
    Permanent link to Research Bank record:
    https://hdl.handle.net/10652/2383
    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.
    Keywords:
    abstract linear proximal support vector machines
    ANZSRC Field of Research:
    170203 Knowledge Representation and Machine Learning
    Copyright Holder:
    Neural Networks
    Rights:
    This digital work is protected by copyright. It may be consulted by you, provided you comply with the provisions of the Act and the following conditions of use: Any use you make of these documents or images must be for research or private study purposes only, and you may not make them available to any other person. You will recognise the author's and publishers rights and give due acknowledgement where appropriate.
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