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    Dynamic class imbalance learning for incremental LPSVM

    Zhu, Lei

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    Lei Zhu_2012-09-14.pdf (323.6Kb)
    Date
    2012
    Citation:
    Zhu, 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 (Unpublished document submitted in partial fulfilment of the requirements for the degree of Master of Computing).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 https://hdl.handle.net/10652/2031
    Permanent link to Research Bank record:
    https://hdl.handle.net/10652/2031
    Abstract
    Abstract 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).
    Keywords:
    abstract linear proximal support vector machines, class imbalance learning, DCIL-IncLPSVM, data mining
    ANZSRC Field of Research:
    170203 Knowledge Representation and Machine Learning
    Degree:
    Master of Computing
    Supervisors:
    Pang, Paul; Chen, Aaron
    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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