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Feature Selection Method Based on Chaotic Maps and Butterfly Optimization Algorithm

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Book cover Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020) (AICV 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1153))

Abstract

Feature selection (FS) is a challenging problem that attracted the attention of many researchers. FS can be considered as an NP hard problem, If dataset contains N features then \(2^{N}\) solutions are generated with each additional feature, the complexity doubles. To solve this problem, we reduce the dimensionality of the feature by extracting the most important features. In this paper we integrate the chaotic maps in the standard butterfly optimization algorithm to increase the diversity and avoid trapping in local minima in this algorithm. The proposed algorithm is called Chaotic Butterfly Optimization Algorithm (CBOA).The performance of the proposed CBOA is investigated by applying it on 16 benchmark datasets and comparing it against six meta-heuristics algorithms. The results show that invoking the chaotic maps in the standard BOA can improve its performance with accuracy more than \(95\% \).

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Author information

Authors and Affiliations

  1. Department of Computer Science, Faculty of Computers and Informatics, Suez Canal University, Ismailia, Egypt

    Asmaa Ahmed Awad, Ahmed Fouad Ali & Tarek Gaber

  2. School of Science, Engineering and Environment, University of Salford, Salford, UK

    Tarek Gaber

  3. Scientific Research Group in Egypt, (SRGE), Cairo, Egypt

    Tarek Gaber

Authors
  1. Asmaa Ahmed Awad
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  2. Ahmed Fouad Ali
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  3. Tarek Gaber
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Corresponding author

Correspondence to Tarek Gaber .

Editor information

Editors and Affiliations

  1. Information Technology Department, Cairo University, Faculty of Computers and Information, Giza, Egypt

    Aboul-Ella Hassanien

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

    Ahmad Taher Azar

  3. Faculty of Computers and Information, Suez Canal University, Ismailia, Egypt

    Tarek Gaber

  4. Departamento de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Guadajalara, Jalisco, Mexico

    Diego Oliva

  5. Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt

    Fahmy M. Tolba

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Awad, A.A., Ali, A.F., Gaber, T. (2020). Feature Selection Method Based on Chaotic Maps and Butterfly Optimization Algorithm. In: Hassanien, AE., Azar, A., Gaber, T., Oliva, D., Tolba, F. (eds) Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020). AICV 2020. Advances in Intelligent Systems and Computing, vol 1153. Springer, Cham. https://doi.org/10.1007/978-3-030-44289-7_16

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