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Feature selection using binary monarch butterfly optimization

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Abstract

Swarm intelligence algorithms have superior performance in searching for the optimal feature subset, where Monarch Butterfly Optimization (MBO) can solve the continuous optimization problem. However, there exist some defects for MBO such as the limited searchable positions, falling into local optimum easily and unsolved binary variables. To address these drawbacks, this paper develops two mechanisms to propose several revisions of binary MBO (BMBO) for metaheuristic feature selection. First, to make MBO suitable to solve the feature selection optimization problems, the S-shaped and V-shaped transfer functions are introduced to convert continuous space into binary, and then force the butterfly to move in the binary search space. Two updated positions of the monarch butterfly population are designed based on these above transfer functions respectively to construct two BMBO models, namely BMBO-S and BMBO-V, as the first mechanism of BMBO. Second, the new step length parameter is proposed to update the position of monarch butterfly individuals. To prevent MBO from falling into the local optimum, the local disturbance and group division strategies are added into MBO to construct new BMBO method. It follows that a mutation rate is employed to enhance the detection stage of BMBO, and the mutation operator-based BMBO (BMBO-M) is designed to avoid the premature convergence of MBO. Third, this fitness function is integrated with the KNN classifier and the weight of the feature subset length to rank the selected feature subset, and a metaheuristic feature selection algorithm with BMBO-M is developed. Experiments applied to nineteen low dimensional UCI datasets and seven high dimensional datasets demonstrate our designed algorithm has great classification efficiency when compared with the other related technologies.

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Acknowledgments

The authors would like to express their sincere appreciation to the anonymous reviewers for their insightful comments, which greatly improved the quality of this paper. This research was funded by the National Natural Science Foundation of China under Grants 62076089, 61772176, and 61976082; the Key Scientific and Technological Project of Henan Province under Grant 212102210136; and the Key Laboratory of Data Science and Intelligence Application, Minnan Normal University (NO. D202004).

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Authors and Affiliations

  1. College of Computer and Information Engineering, Henan Normal University, Xinxiang, 453007, China

    Lin Sun, Shanshan Si, Jing Zhao & Jiucheng Xu

  2. Key Laboratory of Data Science and Intelligence Application, Minnan Normal University, Zhangzhou, 363000, China

    Lin Sun & Yaojin Lin

  3. Engineering Lab of Intelligence Business and Internet of Things of Henan Province, Xinxiang, 453007, China

    Lin Sun

  4. School of Management, Chengdu University of Information Technology, Chengdu, 610000, China

    Zhiying Lv

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  1. Lin Sun
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  2. Shanshan Si
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  4. Jiucheng Xu
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Correspondence to Lin Sun, Jing Zhao or Yaojin Lin.

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Sun, L., Si, S., Zhao, J. et al. Feature selection using binary monarch butterfly optimization. Appl Intell 53, 706–727 (2023). https://doi.org/10.1007/s10489-022-03554-9

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