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A Fuzzy support vector classifier based on Bayesian optimization

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Abstract

In this paper, we have focused on the use of the support vector data description based on kernel-based possibilistic c-means algorithm (PCM) for solving multi-class classification problems. We propose a weighted support vector data description (SVDD) multi-class classification method, which can be used to deal with the outlier sensitivity problem in traditional multi-class classification problems. The proposed method is the robust version of SVDD by assigning a weight to each data point, which represents fuzzy membership degree of the cluster computed by the kernel-based PCM method. Accordingly, this paper presents the multi classification algorithm and gives the simple classification rule, which satisfies Bayesian optimal decision theory. With a simple classification rule, our experimental results show that the proposed method can reduce the effect of outliers and reduce the rate of classification error.

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Authors and Affiliations

  1. Department of Computer, Liaoning Normal University, Dalian, 116029, China

    Yong Zhang

  2. Department of Computer Science and Engineering, Dalian University of Technology, Dalian, 116024, China

    Yong Zhang & Zhong-Xian Chi

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  1. Yong Zhang
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  2. Zhong-Xian Chi
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Correspondence to Yong Zhang.

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Zhang, Y., Chi, ZX. A Fuzzy support vector classifier based on Bayesian optimization. Fuzzy Optim Decis Making 7, 75–86 (2008). https://doi.org/10.1007/s10700-007-9025-7

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  • DOI: https://doi.org/10.1007/s10700-007-9025-7

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