Feature selection method based on chaotic maps and butterfly optimization algorithm

Awad, AA, Ali, AF and Gaber, T ORCID: https://orcid.org/0000-0003-4065-4191 2020, Feature selection method based on chaotic maps and butterfly optimization algorithm , in: International Conference on Artificial Intelligence and Computer Vision (AICV 2020), 8th-10th April 2020, Cairo, Egypt.

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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 2N 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%.

Item Type: Conference or Workshop Item (Paper)
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020)
Publisher: Springer
Series Name: Advances in Intelligent Systems and Computing
ISBN: 9783030442880 (print); 9783030442897 (online)
ISSN: 2194-5357
Related URLs:
Depositing User: Dr. Tarek Gaber
Date Deposited: 03 Mar 2020 12:30
Last Modified: 04 May 2020 13:15
URI: http://usir.salford.ac.uk/id/eprint/56568

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