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A Novel Dynamic Hybridization Method for Best Feature Selection

A Novel Dynamic Hybridization Method for Best Feature Selection

Nassima Dif, Zakaria Elberrichi
Copyright: © 2021 |Volume: 12 |Issue: 2 |Pages: 15
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781799861133|DOI: 10.4018/IJAMC.2021040106
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MLA

Dif, Nassima, and Zakaria Elberrichi. "A Novel Dynamic Hybridization Method for Best Feature Selection." IJAMC vol.12, no.2 2021: pp.85-99. http://doi.org/10.4018/IJAMC.2021040106

APA

Dif, N. & Elberrichi, Z. (2021). A Novel Dynamic Hybridization Method for Best Feature Selection. International Journal of Applied Metaheuristic Computing (IJAMC), 12(2), 85-99. http://doi.org/10.4018/IJAMC.2021040106

Chicago

Dif, Nassima, and Zakaria Elberrichi. "A Novel Dynamic Hybridization Method for Best Feature Selection," International Journal of Applied Metaheuristic Computing (IJAMC) 12, no.2: 85-99. http://doi.org/10.4018/IJAMC.2021040106

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Abstract

Hybrid metaheuristics has received a lot of attention lately to solve combinatorial optimization problems. The purpose of hybridization is to create a cooperation between metaheuristics for better solutions. Most proposed works were interested in static hybridization. The objective of this work is to propose a novel dynamic hybridization method (GPBD) that generates the most suitable sequential hybridization between GA, PSO, BAT, and DE metaheuristics, according to each problem. The authors choose to test this approach for solving the best feature selection problem in a wrapper tactic, performed on face image recognition datasets, with the k-nearest neighbor (KNN) learning algorithm. The comparative study of the metaheuristics and their hybridization GPBD shows that the proposed approach achieved the best results. It was definitely competitive with other filter approaches proposed in the literature. It achieved a perfect accuracy score of 100% for Orl10P, Pix10P, and PIE10P datasets.

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