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

  • Yong ZhangEmail author
  • Hai-Gang LiEmail author
  • Qing Wang
  • Chao Peng
Article

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.

Keywords

Particle swarm optimization Feature selection Unsupervised 

Notes

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).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Information and Control EngineeringChina University of Mining and TechnologyXuzhouChina

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