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Feature Selection Using Sparse Twin Bounded Support Vector Machine

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Neural Information Processing (ICONIP 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12533))

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Abstract

Although twin bounded support machine (TBSVM) has a lower time complexity than support vector machine (SVM), TBSVM has a poor ability to select features. To overcome the shortcoming of TBSVM, we propose a sparse twin bounded support machine (STBSVM) inspired by the sparsity of the \(\ell _1\)-norm. The objective function of STBSVM contains the hinge loss and the \(\ell _1\)-norm terms, both which can induce sparsity. We find solutions in the primal space instead of the dual space and avoid the operation of matrix inversion. All of these can assure the sparsity of STBSVM, or the ability to select features. Experiments carried out on synthetic and UCI datasets show that STBSVM has a good ability to select features and simultaneously enhances the classification performance.

This work was supported in part by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant No. 19KJA550002, by the Six Talent Peak Project of Jiangsu Province of China under Grant No. XYDXX-054, by the Priority Academic Program Development of Jiangsu Higher Education Institutions, and by the Collaborative Innovation Center of Novel Software Technology and Industrialization.

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Author information

Authors and Affiliations

  1. School of Computer Science and Technology and Joint International Research Laboratory of Machine Learning and Neuromorphic Computing, Soochow University, Suzhou, 215006, Jiangsu, China

    Xiaohan Zheng, Li Zhang & Leilei Yan

  2. Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou, 215006, Jiangsu, China

    Li Zhang

Authors
  1. Xiaohan Zheng
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  2. Li Zhang
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  3. Leilei Yan
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Corresponding author

Correspondence to Li Zhang .

Editor information

Editors and Affiliations

  1. Department of AI, Ping An Life, Shenzhen, China

    Haiqin Yang

  2. Faculty of Information Technology, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand

    Kitsuchart Pasupa

  3. City University of Hong Kong, Kowloon, China

    Andrew Chi-Sing Leung

  4. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, Hong Kong

    James T. Kwok

  5. School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand

    Jonathan H. Chan

  6. The Chinese University of Hong Kong, New Territories, Hong Kong

    Irwin King

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Zheng, X., Zhang, L., Yan, L. (2020). Feature Selection Using Sparse Twin Bounded Support Vector Machine. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Lecture Notes in Computer Science(), vol 12533. Springer, Cham. https://doi.org/10.1007/978-3-030-63833-7_30

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  • DOI: https://doi.org/10.1007/978-3-030-63833-7_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-63832-0

  • Online ISBN: 978-3-030-63833-7

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