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Hybrid Binary Butterfly Optimization Algorithm and Simulated Annealing for Feature Selection Problem

Hybrid Binary Butterfly Optimization Algorithm and Simulated Annealing for Feature Selection Problem

Mohd Faizan, Fawaz Alsolami, Raees Ahmad Khan
Copyright: © 2022 |Volume: 13 |Issue: 1 |Pages: 18
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781799885405|DOI: 10.4018/IJAMC.2022010104
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MLA

Faizan, Mohd, et al. "Hybrid Binary Butterfly Optimization Algorithm and Simulated Annealing for Feature Selection Problem." IJAMC vol.13, no.1 2022: pp.1-18. http://doi.org/10.4018/IJAMC.2022010104

APA

Faizan, M., Alsolami, F., & Khan, R. A. (2022). Hybrid Binary Butterfly Optimization Algorithm and Simulated Annealing for Feature Selection Problem. International Journal of Applied Metaheuristic Computing (IJAMC), 13(1), 1-18. http://doi.org/10.4018/IJAMC.2022010104

Chicago

Faizan, Mohd, Fawaz Alsolami, and Raees Ahmad Khan. "Hybrid Binary Butterfly Optimization Algorithm and Simulated Annealing for Feature Selection Problem," International Journal of Applied Metaheuristic Computing (IJAMC) 13, no.1: 1-18. http://doi.org/10.4018/IJAMC.2022010104

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Abstract

Feature selection is performed to eliminate irrelevant features to reduce computational overheads. Metaheuristic algorithms have become popular for the task of feature selection due to their effectiveness and flexibility. Hybridization of two or more such metaheuristics has become popular in solving optimization problems. In this paper, we propose a hybrid wrapper feature selection technique based on binary butterfly optimization algorithm (bBOA) and Simulated Annealing (SA). The SA is combined with the bBOA in a pipeline fashion such that the best solution obtained by the bBOA is passed on to the SA for further improvement. The SA solution improves the best solution obtained so far by searching in its neighborhood. Thus the SA tries to enhance the exploitation property of the bBOA. The proposed method is tested on twenty datasets from the UCI repository and the results are compared with five popular algorithms for feature selection. The results confirm the effectiveness of the hybrid approach in improving the classification accuracy and selecting the optimal feature subset.