A review of Harmony Search algorithm-based feature selection method for classification

Abstract

In high dimensional datasets, feature selection plays a significant task for dimensionality reduction and classification. During feature selection process, only the most relevant features in the datasets will be selected. A good feature selection technique can reduce computation cost and increased classification performance. In this paper, we discuss the performance of feature selection with Harmony Search (HS) algorithm for classification in various applications. From the review, it can be concluded that feature selection with HS gives a good performance in many research areas as compared to other nature inspired metaheuristics algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO).