Improved coral reefs optimization with adaptive $$\beta $$-hill climbing for feature selection

Abstract

For any classification problem, the dimension of the feature vector used for classification has great importance. This is because, in a high-dimensional feature vector, it is found that some are non-informative or even redundant as they do not contribute to the learning process of the classifier. Rather, they may be the reason for low classification accuracy and high training time of the learning model. To address this issue, researchers apply various feature selection (FS) methods as found in the literature. In recent years, meta-heuristic algorithms have been proven to be effective in solving FS problems. The Coral Reefs Optimizer (CRO) which is a cellular type evolutionary algorithms has good tuning between its exploration and exploitation ability. This has motivated us to present an improved version of CRO with the inclusion of adaptive $$\beta $$ -hill climbing to increase the exploitation ability of CRO. The proposed method is assessed on 18 standard UCI-datasets by means of three distinct classifiers, KNN, Random Forest and Naive Bayes classifiers. It is also analyzed with 10 state-of-the-art meta-heuristics FS procedure, and the outputs show an excellent performance of the proposed FS method reaching better results than the previous methods considered here for comparison. The source code of this work is publicly available at https://github.com/ahmed-shameem/Projects .