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IMF-PSO: A Particle Swarm Optimization Algorithm for Feature Selection in Classification

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Technologies and Applications of Artificial Intelligence (TAAI 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2074))

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Abstract

Feature selection is an important step in classification. Its goal is to find a set of features that can lead to high classification accuracy with a smaller number of features. This paper addresses feature selection as an optimization problem and solves it by a particle swarm optimization (PSO)-based approach. In the proposed PSO, we adopt three algorithmic components to enhance its performance: feature space adjustment, multi-swarm search, and local-best-guided improvement. We examine the effects of these components using seven data sets from the UCI repository. We also compare our algorithm with two existing algorithms. Experimental results show that the incorporated algorithmic components improve the algorithm performance and our algorithm outperforms the compared algorithms.

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Notes

  1. 1.

    https://github.com/XuesenYang/2-D-Particle-Swarm-based-feature-selection.

  2. 2.

    https://github.com/xueyunuist/Self-Adaptive-Particle-Swarm-Optimization-for-Large-Scale-Feature-Selection-in-Classification.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Taiwan Normal University, Taipei, Taiwan (R.O.C.)

    Cheng-Ju Lu & Tsung-Che Chiang

Authors
  1. Cheng-Ju Lu
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  2. Tsung-Che Chiang
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Correspondence to Tsung-Che Chiang .

Editor information

Editors and Affiliations

  1. National Yunlin University of Science and Technology, Douliou, Taiwan

    Chao-Yang Lee

  2. National Yunlin University of Science and Technology, Douliou, Taiwan

    Chun-Li Lin

  3. National Yunlin University of Science and Technology, Douliou, Taiwan

    Hsuan-Ting Chang

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Lu, CJ., Chiang, TC. (2024). IMF-PSO: A Particle Swarm Optimization Algorithm for Feature Selection in Classification. In: Lee, CY., Lin, CL., Chang, HT. (eds) Technologies and Applications of Artificial Intelligence. TAAI 2023. Communications in Computer and Information Science, vol 2074. Springer, Singapore. https://doi.org/10.1007/978-981-97-1711-8_8

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  • DOI: https://doi.org/10.1007/978-981-97-1711-8_8

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-1710-1

  • Online ISBN: 978-981-97-1711-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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