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Brain storm optimization for feature selection using new individual clustering and updating mechanism

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Abstract

Feature selection is an important preprocessing technique for data. Brain storm optimization (BSO) is one of the latest swarm intelligence algorithms, which simulates the collective behavior of human beings. However, traditional updating mechanisms in BSO limit its application in feature selection. We study a new individual clustering technology and two individual updating mechanisms in BSO for developing novel feature selection algorithms with the purpose of maximizing the classification performance. The proposed individual updating mechanisms are compared with each other. The more promising updating mechanism and the new individual clustering technology are combined into the BSO framework to form a new wrapper feature selection algorithm, called BBSOFS. Compared with existing algorithms including particle swarm optimization, firefly algorithm and BSO algorithm, experimental results on benchmark datasets show that with the help of the proposed individual clustering and updating mechanism, the proposed BBSOFS algorithm can obtain feature subsets with good classification accuracy.

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Acknowledgements

This work was supported by the Fundamental Research Funds for the Central Universities (No. 2018XKQYMS03).

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  1. School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China

    Wan-qiu Zhang, Yong Zhang & Chao Peng

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  1. Wan-qiu Zhang
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  2. Yong Zhang
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  3. Chao Peng
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Correspondence to Yong Zhang.

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Zhang, Wq., Zhang, Y. & Peng, C. Brain storm optimization for feature selection using new individual clustering and updating mechanism. Appl Intell 49, 4294–4302 (2019). https://doi.org/10.1007/s10489-019-01513-5

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