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Fuzzy SVM with a new fuzzy membership function

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Abstract

It is known that with a proper fuzzy membership function, a fuzzy support vector machine can effectively reduce the effects of outliers when solving the classification problem. In this paper, a new fuzzy membership function is proposed to the nonlinear fuzzy support vector machine. The fuzzy membership is calculated in the feature space and is represented by kernels. This method gives good performance on reducing the effects of outliers and significantly improves the classification accuracy and generalization.

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Acknowledgements

This work was supported by National Science Foundation of China under Grant 60471055 and Specialized Research Fund for the Doctoral Program of Higher Education under Grant 20040614017.

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Authors and Affiliations

  1. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, People’s Republic of China

    Xiufeng Jiang, Zhang Yi & Jian Cheng Lv

Authors
  1. Xiufeng Jiang
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  2. Zhang Yi
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  3. Jian Cheng Lv
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Corresponding author

Correspondence to Zhang Yi.

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Jiang, X., Yi, Z. & Lv, J.C. Fuzzy SVM with a new fuzzy membership function. Neural Comput & Applic 15, 268–276 (2006). https://doi.org/10.1007/s00521-006-0028-z

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  • DOI: https://doi.org/10.1007/s00521-006-0028-z

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