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Algorithm of Fuzzy Support Vector Machine based on a Piecewise Linear Fuzzy Weight Method

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  • Yong-bin Yuan

    (College of Electrical Engineering and Automation Fuzhou University, Fuzhou, China)

  • Sheng Lan

    (College of Electrical Engineering and Automation Fuzhou University, Fuzhou, China)

  • Xu Yu

    (School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, China)

  • Miao Yu

    (The College of Textiles and Fashion, Qingdao University, Qingdao, China)

Abstract

This article describes how fuzzy support vector machines (FSVMs) function well with good anti-noise performance, which receives the attention of many experts. However, the traditional center-distance fuzzy weight assignment method assigns support vectors with a small value of a membership degree and this weakens the role of support vectors in classification. In this article, a piecewise linear fuzzy weight computing method is proposed, in which boundary samples are assigned with a larger value of membership degree and samples far from the mean vector are assigned a smaller value of membership degree. The proposed method has a good classification performance, because the influence of noise samples is weakened and meanwhile the support vectors are paid much more attention. The experiments on the UCI database and MNIST data set fully verify the effectiveness of the proposed algorithm.

Suggested Citation

  • Yong-bin Yuan & Sheng Lan & Xu Yu & Miao Yu, 2018. "Algorithm of Fuzzy Support Vector Machine based on a Piecewise Linear Fuzzy Weight Method," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 12(2), pages 62-76, April.
    • Handle: RePEc:igg:jcini0:v:12:y:2018:i:2:p:62-76
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