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Feature selection optimized by the artificial immune algorithm based on genome shuffling and conditional lethal mutation

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Abstract

Improving classification performance is an essential goal for various practical applications. Feature selection has become an important data preprocessing step in machine learning systems. However, many effective methods based on heuristic search strategies have the problem of high running costs. This paper proposes an efficient multiobjective feature selection method based on artificial immune algorithm optimization. It introduces a clone selection algorithm to explore the search space of optimal feature subsets. According to the target requirements of feature selection, combined with biological research results, this method introduces genome shuffling technology and a conditional lethal mutation mechanism to improve the search performance of the algorithm. Experimental comparisons are conducted on 21 benchmark datasets with 17 advanced feature selection methods in terms of classification accuracy, the number of feature subsets, and computational cost. The results show that the algorithm achieves the smallest number of selected features (only 3.26% compared to the lowest) and better average classification accuracy with a much lower average computational cost than others (only 3.62% compared to the lowest).

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Acknowledgements

This work was supported in part by the National Key Research and Development Program of China (No. 2020YFB1805400); in part by the National Natural Science Foundation of China (No.U1736212 , U19A2068, 62032002, and 62002248); in part by the China Postdoctoral Science Foundation (No. 2019TQ0217, 2020M673277, and 2020M683345); in part by the Provincial Key Research and Development Program of Sichuan (No. 20ZDYF3145); in part by the Fundamental Research Funds for the Central Universities (No. Y J201933); in part by the China International Postdoctoral Exchange Fellowship Program (Talent-Introduction).

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  1. College of Cybersecurity, Sichuan University, Chengdu, 610065, People’s Republic of China

    Yongbin Zhu, Tao Li & Xiaolong Lan

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Correspondence to Tao Li.

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Zhu, Y., Li, T. & Lan, X. Feature selection optimized by the artificial immune algorithm based on genome shuffling and conditional lethal mutation. Appl Intell 53, 13972–13992 (2023). https://doi.org/10.1007/s10489-022-03971-w

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