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BSSFS: binary sparrow search algorithm for feature selection

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Abstract

Swarm intelligence algorithms can efficiently solve feature selection optimization problems for classification, and their classification performance is also excellent. The Sparrow Search Algorithm (SSA) has recently become a novel optimization method, which has the advantages of fewer parameters, a simple structure, and ease of implementation. Unfortunately, the traditional SSA has certain difficulties, trapping into a local optimum easily, and has a weakness in convergence. In this work, a novel SSA is studied to further develop two types of binary SSA for feature selection (BSSFS) to address optimization problems. First, to make the initial population of sparrows distributed as evenly as possible in the search space and augment the diversity of sparrows, a cubic chaotic mapping scheme needs to be introduced to initialize the sparrow population, and a reverse learning scheme is employed to enhance the global search ability of SSA. Second, the nonlinear adaptive inertia weight and the improved control parameters of step size are used to modify the position update formula of the sparrows and avert trapping into a local optimum. By integrating these strategies, a novel SSA (NSSA in short) is designed in this work. Third, NSSA is combined with the S- and V-shaped transfer functions, and the fitness function, which is raised on account of the set size of selected features and the classification error rate, are employed to design two types of BSSFS with the S- and V-shaped transfer functions, shortened as FSBSS and FSBSV, respectively. Finally, the optimization results for 18 classical benchmark functions illustrate that the optimization effectiveness of the NSSA is superior to that of other methods, and the experimental results on 10 low-dimensional and 10 high-dimensional datasets demonstrate that the FSBSV can outdo other comparative algorithms in terms of the classification effectiveness and robustness.

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Acknowledgements

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, 61976082, and 61976120; the Excellent Science and Technology Innovation Team of Henan Normal University under Grant 2021TD05; and the Natural Science Key Foundation of Jiangsu Education Department under Grant 21KJA510004.

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Authors and Affiliations

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

    Lin Sun, Shanshan Si, Jiucheng Xu & Yan Zhang

  2. School of Information Science and Technology, Nantong University, Nantong, 226019, China

    Weiping Ding

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

    Lin Sun

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  1. Lin Sun
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  4. Jiucheng Xu
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Correspondence to Lin Sun or Weiping Ding.

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Sun, L., Si, S., Ding, W. et al. BSSFS: binary sparrow search algorithm for feature selection. Int. J. Mach. Learn. & Cyber. 14, 2633–2657 (2023). https://doi.org/10.1007/s13042-023-01788-8

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