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Feature Selection Based on a Modified Adaptive Human Learning Optimization Algorithm

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Intelligent Equipment, Robots, and Vehicles (LSMS 2021, ICSEE 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1469))

Abstract

This paper proposes a new wrapper feature selection method based on the modified adaptive Human Learning Optimization (MAHLO) algorithm and Support Vector Machine (SVM). To achieve better results, the initialization and random learning strategies are modified in MAHLO to solve the feature selection problem more efficiently and the adaptive strategies are used to enhance the performance and relieve the effort of parameter setting. Besides, a two-stage evaluation function is adopted to eliminate the useless and redundant features, which is easier to operate in applications. The simulation results indicate that MAHLO can solve high-dimensional feature selection problems more efficiently, and the classification results on the UCI benchmark problems further demonstrate the efficiency and the advantage of the proposed MAHLO-based wrapper feature selection method.

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Acknowledgments

This work is supported by National Key Research and Development Program of China (No. 2019YFB1405500), National Natural Science Foundation of China (Grant No. 92067105 & 61833011), Key Project of Science and Technology Commission of Shanghai Municipality under Grant No. 19510750300 & 19500712300, and 111 Project under Grant No. D18003.

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Authors and Affiliations

  1. Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronics Engineering and Automation, Shanghai University, Shanghai, 200072, China

    Siyue Yu, Yihao Jia, Xuelian Hu & Ling Wang

  2. Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC, 27606, USA

    Haoqi Ni

Authors
  1. Siyue Yu
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  2. Yihao Jia
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  3. Xuelian Hu
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  4. Haoqi Ni
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  5. Ling Wang
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Corresponding author

Correspondence to Ling Wang .

Editor information

Editors and Affiliations

  1. Swinburne University of Technology, Melbourne, VIC, Australia

    Qinglong Han

  2. Queen's University Belfast, Belfast, UK

    Sean McLoone

  3. Shanghai University, Shanghai, China

    Chen Peng

  4. Qingdao University of Science and Technology, Qingdao, China

    Baolin Zhang

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Yu, S., Jia, Y., Hu, X., Ni, H., Wang, L. (2021). Feature Selection Based on a Modified Adaptive Human Learning Optimization Algorithm. In: Han, Q., McLoone, S., Peng, C., Zhang, B. (eds) Intelligent Equipment, Robots, and Vehicles. LSMS ICSEE 2021 2021. Communications in Computer and Information Science, vol 1469. Springer, Singapore. https://doi.org/10.1007/978-981-16-7213-2_76

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  • DOI: https://doi.org/10.1007/978-981-16-7213-2_76

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-7212-5

  • Online ISBN: 978-981-16-7213-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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