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International Conference on Bio-Inspired Computing: Theories and Applications

BIC-TA 2021: Bio-Inspired Computing: Theories and Applications pp 71–87Cite as

Attribute Selection Method Based on Artificial Bee Colony Algorithm and Neighborhood Discrimination Matrix Optimization

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1565))

Abstract

At present, the existing attribute reduction algorithm combining artificial bee colony and neighborhood rough set basically uses the attribute dependence and the number of attribute subsets as parameters to construct the fitness function, while ignoring the role of heuristic information. As a result, the number of bee colony iterations increases, and the convergence speed is slow. Aiming at this kind of problem, an improved method is proposed. First, a discernibility matrix under the neighborhood rough set is defined; secondly, an attribute importance measurement method of the discernibility matrix under the neighborhood decision system is proposed; The attribute importance of the domain discrimination matrix constructs a new fitness function for the heuristic factor; finally, an attribute selection algorithm for artificial bee colony algorithm and neighborhood discrimination matrix importance optimization is designed. Compared with the original algorithm, the new method reduces the number of generations, accelerates the convergence speed, and retains the minimum attribute reduction collected during each iteration, and multiple minimum attribute reductions can be obtained. The experimental results on the UCI data set prove the feasibility and effectiveness of the algorithm.

Supported by the National Natural Science Foundation of China (Nos. 61562061) and Technology Project of Ministry of Education of Jiangxi province of China (No.GJJ211920, GJJ170995).

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Authors and Affiliations

  1. Nanchang Institute of Technology, Nanchang, 330000, Jiangxi, China

    Yuxuan Ji, Jun Ye, Zhenyu Yang, Jiaxin Ao & Lei Wang

  2. Key Laboratory of Water Information Cooperative Perception and Intelligent Processing of Jiangxi Province, Nanchang, 330000, Jiangxi, China

    Jun Ye & Lei Wang

Authors
  1. Yuxuan Ji
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  2. Jun Ye
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  3. Zhenyu Yang
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  4. Jiaxin Ao
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  5. Lei Wang
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Corresponding author

Correspondence to Jun Ye .

Editor information

Editors and Affiliations

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

    Linqiang Pan

  2. Taiyuan University of Science and Technology, Taiyuan, China

    Zhihua Cui

  3. Taiyuan University of Science and Technology, Taiyuan, China

    Jianghui Cai

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

    Lianghao Li

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Ji, Y., Ye, J., Yang, Z., Ao, J., Wang, L. (2022). Attribute Selection Method Based on Artificial Bee Colony Algorithm and Neighborhood Discrimination Matrix Optimization. In: Pan, L., Cui, Z., Cai, J., Li, L. (eds) Bio-Inspired Computing: Theories and Applications. BIC-TA 2021. Communications in Computer and Information Science, vol 1565. Springer, Singapore. https://doi.org/10.1007/978-981-19-1256-6_6

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  • DOI: https://doi.org/10.1007/978-981-19-1256-6_6

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-1255-9

  • Online ISBN: 978-981-19-1256-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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