Feature Selection Using Metaheuristic Algorithms: Concept, Applications and Population Based Comparison

Abstract

Technologies like machine learning in the current times, have emerged as capable domains of research in Computer Science. In Machine learning, the system is trained on the basis of the available dataset; the dataset may contain many redundant and irrelevant features which may require more memory for storage and also increases the cost of computation. Selection of best features enhances the accuracy of data classification along with working on smallest amount of features is considered as an optimization problem. Metaheuristic algorithms in current times have been used far and wide unravel various optimization problems. In this context, this study aims to discuss the solution of feature selection problem using metaheuristic algorithms and presents a population based comparison of four metaheuristic algorithms for extracting smallest feature subset with utmost accuracy.