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Dynamic Mutation Based Pareto Optimization for Subset Selection

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Intelligent Computing Methodologies (ICIC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10956))

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Abstract

Subset selection that selects the best k variables from n variables is a fundamental problem in many areas. Pareto optimization for subset selection (called POSS) is a recently proposed approach for subset selection based on Pareto optimization and has shown good approximation performances. In the reproduction of POSS, it uses a fixed mutation rate, which may make POSS get trapped in local optimum. In this paper, we propose a new version of POSS by using a dynamic mutation rate, briefly called DM-POSS. We prove that DM-POSS can achieve the best known approximation guarantee for the application of sparse regression in polynomial time and show that DM-POSS can also empirically perform well.

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Notes

  1. 1.

    The datasets are from https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/.

  2. 2.

    The datasets are from https://snap.stanford.edu/data/.

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Author information

Authors and Affiliations

  1. Anhui Province Key Lab of Big Data Analysis and Application, University of Science and Technology of China, Hefei, 230027, China

    Mengxi Wu & Chao Qian

  2. Shenzhen Key Lab of Computational Intelligence, Southern University of Science and Technology, Shenzhen, 518055, China

    Ke Tang

Authors
  1. Mengxi Wu
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  2. Chao Qian
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  3. Ke Tang
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Corresponding author

Correspondence to Mengxi Wu .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Indian Institute of Technology Madras, Chennai, India

    M. Michael Gromiha

  3. Inha University, Incheon, Korea (Republic of)

    Kyungsook Han

  4. Liverpool John Moores University, Liverpool, United Kingdom

    Abir Hussain

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Wu, M., Qian, C., Tang, K. (2018). Dynamic Mutation Based Pareto Optimization for Subset Selection. In: Huang, DS., Gromiha, M., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2018. Lecture Notes in Computer Science(), vol 10956. Springer, Cham. https://doi.org/10.1007/978-3-319-95957-3_4

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  • DOI: https://doi.org/10.1007/978-3-319-95957-3_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-95956-6

  • Online ISBN: 978-3-319-95957-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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