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A Binary Superior Tracking Artificial Bee Colony with Dynamic Cauchy Mutation for Feature Selection

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  • Xianghua Chu
  • Shuxiang Li
  • Da Gao
  • Wei Zhao
  • Jianshuang Cui
  • Linya Huang

Abstract

This paper aims to propose an improved learning algorithm for feature selection, termed as binary superior tracking artificial bee colony with dynamic Cauchy mutation (BSTABC-DCM). To enhance exploitation capacity, a binary learning strategy is proposed to enable each bee to learn from the superior individuals in each dimension. A dynamic Cauchy mutation is introduced to diversify the population distribution. Ten datasets from UCI repository are adopted as test problems, and the average results of cross-validation of BSTABC-DCM are compared with other seven popular swarm intelligence metaheuristics. Experimental results demonstrate that BSTABC-DCM could obtain the optimal classification accuracy and select the best representative features for the UCI problems.

Suggested Citation

  • Xianghua Chu & Shuxiang Li & Da Gao & Wei Zhao & Jianshuang Cui & Linya Huang, 2020. "A Binary Superior Tracking Artificial Bee Colony with Dynamic Cauchy Mutation for Feature Selection," Complexity, Hindawi, vol. 2020, pages 1-13, November.
    • Handle: RePEc:hin:complx:8864315
    DOI: 10.1155/2020/8864315
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    Cited by:

    1. Marcelo Becerra-Rozas & José Lemus-Romani & Felipe Cisternas-Caneo & Broderick Crawford & Ricardo Soto & Gino Astorga & Carlos Castro & José García, 2022. "Continuous Metaheuristics for Binary Optimization Problems: An Updated Systematic Literature Review," Mathematics, MDPI, vol. 11(1), pages 1-32, December.

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