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A filter-based bare-bone particle swarm optimization algorithm for unsupervised feature selection

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Abstract

Due to good exploration capability, particle swarm optimization (PSO) has shown advantages on solving supervised feature selection problems. Compared with supervised and semi-supervised cases, unsupervised feature selection becomes very difficult as a result of no label information. This paper studies a novel PSO-based unsupervised feature selection method, called filter-based bare-bone particle swarm optimization algorithm (FBPSO). Two filter-based strategies are proposed to speed up the convergence of the algorithm. One is a space reduction strategy based on average mutual information, which is used to remove irrelevant and weakly relevant features fast; another is a local filter search strategy based on feature redundancy, which is used to improve the exploitation capability of the swarm. And, a feature similarity-based evaluation function and a parameter-free update strategy of particle are introduced to enhance the performance of FBPSO. Experimental results on some typical datasets confirm superiority and effectiveness of the proposed FBPSO.

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Acknowledgements

This work was jointly supported by the National Natural Science Foundation of China (No. 61876185), and Six Talents Peaks Project of Jiangsu Province (No. DZXX-053).

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Authors and Affiliations

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

    Yong Zhang, Hai-Gang Li, Qing Wang & Chao Peng

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  1. Yong Zhang
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  2. Hai-Gang Li
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  3. Qing Wang
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  4. Chao Peng
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Correspondence to Yong Zhang or Hai-Gang Li.

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Zhang, Y., Li, HG., Wang, Q. et al. A filter-based bare-bone particle swarm optimization algorithm for unsupervised feature selection. Appl Intell 49, 2889–2898 (2019). https://doi.org/10.1007/s10489-019-01420-9

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