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Fuzzy Support Vector Machines Based on Spherical Regions

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Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3971))

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Abstract

Fuzzy Support Vector Machines (FSVMs) based on spherical regions are proposed in this paper. Firstly, the center of the spherite is determined by all the training data. Secondly, the membership functions are defined with the distances between each data and the center of the spherite. Thirdly, using the suitable parameter λ, FSVMs are formed on the spherical regions. One-against-one decision strategy of FSVMs is adopted so that the proposed FSVMs can be extended to solve multi-class problems. In order to verify the superiority of the proposed FSVMs, the traditional two-class and multi-class problems of machine learning benchmark datasets are used to test the feasibility and performance of the proposed FSVMs. The experiment results indicate that the new approach not only has higher precision but also downsizes the number of training data and reduces the running time.

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Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Wuhan University of Technology, Wuhan, 430070, P. R. China

    Hong-Bing Liu, Sheng-Wu Xiong & Xiao-Xiao Niu

  2. Department of Computer Science, Xinyang Normal University, Xinyang, 464000, P. R. China

    Hong-Bing Liu

Authors
  1. Hong-Bing Liu
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  2. Sheng-Wu Xiong
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  3. Xiao-Xiao Niu
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd., 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Liu, HB., Xiong, SW., Niu, XX. (2006). Fuzzy Support Vector Machines Based on Spherical Regions. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11759966_139

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  • DOI: https://doi.org/10.1007/11759966_139

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34439-1

  • Online ISBN: 978-3-540-34440-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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