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Feature selection via a novel chaotic crow search algorithm

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Abstract

Crow search algorithm (CSA) is a new natural inspired algorithm proposed by Askarzadeh in 2016. The main inspiration of CSA came from crow search mechanism for hiding their food. Like most of the optimization algorithms, CSA suffers from low convergence rate and entrapment in local optima. In this paper, a novel meta-heuristic optimizer, namely chaotic crow search algorithm (CCSA), is proposed to overcome these problems. The proposed CCSA is applied to optimize feature selection problem for 20 benchmark datasets. Ten chaotic maps are employed during the optimization process of CSA. The performance of CCSA is compared with other well-known and recent optimization algorithms. Experimental results reveal the capability of CCSA to find an optimal feature subset which maximizes the classification performance and minimizes the number of selected features. Moreover, the results show that CCSA is superior compared to CSA and the other algorithms. In addition, the experiments show that sine chaotic map is the appropriate map to significantly boost the performance of CSA.

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Authors and Affiliations

  1. Faculty of Computers and Information, Cairo University, Cairo, Egypt

    Gehad Ismail Sayed & Aboul Ella Hassanien

  2. Faculty of Computers and Information, Benha University, Banha, Egypt

    Ahmad Taher Azar

  3. Nanoelectronics Integrated Systems Center (NISC), Nile University, Giza, Egypt

    Ahmad Taher Azar

Authors
  1. Gehad Ismail Sayed
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  2. Aboul Ella Hassanien
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  3. Ahmad Taher Azar
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Correspondence to Gehad Ismail Sayed.

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Sayed, G.I., Hassanien, A.E. & Azar, A.T. Feature selection via a novel chaotic crow search algorithm. Neural Comput & Applic 31, 171–188 (2019). https://doi.org/10.1007/s00521-017-2988-6

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