Binary coyote optimization algorithm for feature selection

作者：

Highlights：

• A binary coyote optimization algorithm for feature selection problem is proposed.

• The binarization method is based on hyperbolic transfer function.

• We test the proposed wrapper with the Naïve Bayes classifier using seven data sets.

• The wrapper presents high training accuracy.

• The wrapper shows relatively low standard deviation and subsets with few features.

摘要

•A binary coyote optimization algorithm for feature selection problem is proposed.•The binarization method is based on hyperbolic transfer function.•We test the proposed wrapper with the Naïve Bayes classifier using seven data sets.•The wrapper presents high training accuracy.•The wrapper shows relatively low standard deviation and subsets with few features.

论文关键词：Wrapper feature selection,Classification,Coyote optimization algorithm (COA),Bio-inspired optimization;Metaheuristics,Binary COA

论文评审过程：Received 7 August 2019, Revised 21 February 2020, Accepted 21 May 2020, Available online 1 June 2020, Version of Record 13 June 2020.

论文官网地址：https://doi.org/10.1016/j.patcog.2020.107470