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A Surrogate-Assisted Ensemble Particle Swarm Optimizer for Feature Selection Problems

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Advances in Swarm Intelligence (ICSI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13344))

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Abstract

For feature selection problems on high-dimensional data, this paper proposes a surrogate-assisted ensemble particle swarm feature selection algorithm, by combining the global search ability of evolutionary algorithm with the fast search ability of filter method. A space partition method based on K-nearest neighbors is proposed to select represent samples as surrogate. The proposed ensemble algorithm is applied to several datasets. Experimental results show that the proposed algorithm can obtain feature subsets with higher classification accuracy in less computing time.

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Authors and Affiliations

  1. China University of Mining and Technology, Xuzhou, 221116, China

    Jiang Zhi, Zhang Yong, Song **an-fang & He Chunlin

Authors
  1. Jiang Zhi
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  2. Zhang Yong
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  3. Song **an-fang
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  4. He Chunlin
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Corresponding author

Correspondence to Jiang Zhi .

Editor information

Editors and Affiliations

  1. Peking University, Bei**g, China

    Ying Tan

  2. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  3. Shenzhen University, Shenzhen, China

    Ben Niu

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Zhi, J., Yong, Z., **an-fang, S., Chunlin, H. (2022). A Surrogate-Assisted Ensemble Particle Swarm Optimizer for Feature Selection Problems. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2022. Lecture Notes in Computer Science, vol 13344. Springer, Cham. https://doi.org/10.1007/978-3-031-09677-8_14

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  • DOI: https://doi.org/10.1007/978-3-031-09677-8_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-09676-1

  • Online ISBN: 978-3-031-09677-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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