Improved Local Search Based Grey Wolf Optimization for Feature Selection

Abstract

These days’ technologies like machine learning have come into sight as promising areas of research, in which the system is trained using a dataset; that may comprise redundant and inappropriate features and may require more memory. Feature Selection aids to increase the accuracy during the process of classification while considering minimum number of features, that can be modeled as an optimization task. Currently, metaheuristic category of algorithms is being widely used by researchers to solve various optimization problems. In this context, this research proposes an improved variant of Grey Wolf optimization Algorithm using mutation based local search for getting into the bottom of the problem of selecting smallest set of features while increasing the accuracy. Moreover, the presented algorithm is weighed against several other algorithms for solving problem of selecting smallest set of features with increased accuracy.