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

  • Wan-qiu Zhang
  • Yong ZhangEmail author
  • Chao Peng
Article

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.

Keywords

Brain storm optimization Binary Feature selection Individual clustering 

Notes

Acknowledgements

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

Compliance with ethical standards

Conflict of interest

The authors declare that the writing of this paper does not cause any competing interests to them.

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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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