Binary coyote optimization algorithm for feature selection

doi:10.1016/j.patcog.2020.107470

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Binary coyote optimization algorithm for feature selection

2020-11-01

PRE

Rodrigo Clemente Thom de Souza

Camila Andrade de Macedo *

Leandro dos Santos Coelho

Juliano Pierezan

Viviana Cocco Mariani

DOI：10.1016/j.patcog.2020.107470

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Abstract

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The Coyote Optimization Algorithm (COA) is a bio-inspired optimization algorithm based on the intelligent behavior of coyotes. COA was proposed recently and it considers the social organization of the coyotes and its adaptation to the environment in order to solve continuous optimization problems. In addition, it is a population-based algorithm and it can be classified as both, swarm intelligence and evolutionary heuristics, because contributes with a different algorithmic structure. This paper proposes a binary version of the COA, named Binary COA (BCOA) applying to select the optimal feature subset for classification, based on the hyperbolic transfer function in a wrapper model. By this way, the features are selected based on the performance evaluation of a classification algorithm. We tested the effectiveness of the BCOA wrapper with the Naive Bayes classifier and were used seven public domain benchmark datasets to compare the proposed approach in terms of classification accuracy, number of selected features and computational cost with other state-of-art algorithms of the literature. The results shown that BCOA was able to find subsets with few features while it still performs well in terms of classification accuracy. (C) 2020 Elsevier Ltd. All rights reserved.

Keywords:

Wrapper feature selection

Classification

Coyote optimization algorithm (COA)

Bio-inspired optimization

Metaheuristics

Binary COA

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