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International Conference on Bio-Inspired Computing: Theories and Applications

BIC-TA 2019: Bio-inspired Computing: Theories and Applications pp 649–661Cite as

An Improved Multi-objective Particle Swarm Optimization with Adaptive Penalty Value for Feature Selection

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1159))

Abstract

Feature selection is an important data-preprocessing technique to eliminate the features with low contributions in classification. Currently, many researches focus their interests on the combination of feature selection and multi-objective particle swarm optimization (PSO). However, these methods exist the problems of large search space and the loss of global search. This paper proposes a multi-objective particle swarm optimization with the method called APPSOFS that the leader archive is updated by an adaptive penalty value mechanism based on PBI parameter. Meanwhile, the random generalized opposition-based learning point (GOBL-R) point is adopted to help jump out of local optima. The proposed method is compared with three multi-objective PSO and MOEA/D on six benchmark datasets. The results have demonstrated that the proposed method has better performance on feature selection.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China [Nos. 61976108 and 61572241], the National Key R&D Program of China [No. 2017YFC0806600], the Foundation of the Peak of Six Talents of Jiangsu Province [No. 2015-DZXX-024] and the Fifth “333 High Level Talented Person Cultivating Project” of Jiangsu Province [No. (2016) III-0845].

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Authors and Affiliations

  1. School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, China

    Wentao Chen & Fei Han

Authors
  1. Wentao Chen
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  2. Fei Han
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Correspondence to Wentao Chen .

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Editors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  2. Zhengzhou University, Zhengzhou, China

    Jing Liang

  3. Zhongyuan University of Technology, Zhengzhou, China

    Boyang Qu

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Chen, W., Han, F. (2020). An Improved Multi-objective Particle Swarm Optimization with Adaptive Penalty Value for Feature Selection. In: Pan, L., Liang, J., Qu, B. (eds) Bio-inspired Computing: Theories and Applications. BIC-TA 2019. Communications in Computer and Information Science, vol 1159. Springer, Singapore. https://doi.org/10.1007/978-981-15-3425-6_51

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  • DOI: https://doi.org/10.1007/978-981-15-3425-6_51

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-3424-9

  • Online ISBN: 978-981-15-3425-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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