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An Improved Binary Crayfish Optimization Algorithm for Handling Feature Selection Task in Supervised Classification

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  • Shaymaa E. Sorour

    (Department of Management Information Systems, School of Business, King Faisal University, Alhufof 31982, Saudi Arabia
    Faculty of Specific Education, Kafrelsheikh University, Kafrelsheikh 33511, Egypt)

  • Lamia Hassan

    (Department of Management Information Systems, School of Business, King Faisal University, Alhufof 31982, Saudi Arabia)

  • Amr A. Abohany

    (Faculty of Computers and Information, Kafrelsheikh University, Kafrelsheikh 33511, Egypt)

  • Reda M. Hussien

    (Faculty of Computers and Information, Kafrelsheikh University, Kafrelsheikh 33511, Egypt)

Abstract

Feature selection (FS) is a crucial phase in data mining (DM) and machine learning (ML) tasks, aimed at removing uncorrelated and redundant attributes to enhance classification accuracy. This study introduces an improved binary crayfish optimization algorithm (IBCOA) designed to tackle the FS problem. The IBCOA integrates a local search strategy and a periodic mode boundary handling technique, significantly improving its ability to search and exploit the feature space. By doing so, the IBCOA effectively reduces dimensionality, while improving classification accuracy. The algorithm’s performance was evaluated using support vector machine (SVM) and k-nearest neighbor (k-NN) classifiers on eighteen multi-scale benchmark datasets. The findings showed that the IBCOA performed better than nine recent binary optimizers, attaining 100% accuracy and decreasing the feature set size by as much as 0.8. Statistical evidence supports that the proposed IBCOA is highly competitive according to the Wilcoxon rank sum test (alpha = 0.05). This study underscores the IBCOA’s potential for enhancing FS processes, providing a robust solution for high-dimensional data challenges.

Suggested Citation

  • Shaymaa E. Sorour & Lamia Hassan & Amr A. Abohany & Reda M. Hussien, 2024. "An Improved Binary Crayfish Optimization Algorithm for Handling Feature Selection Task in Supervised Classification," Mathematics, MDPI, vol. 12(15), pages 1-41, July.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:15:p:2364-:d:1445421
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    References listed on IDEAS

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    1. Luis Rios & Nikolaos Sahinidis, 2013. "Derivative-free optimization: a review of algorithms and comparison of software implementations," Journal of Global Optimization, Springer, vol. 56(3), pages 1247-1293, July.
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