Feature Subset Selection Approach by Gray-Wolf Optimization

Feature selection algorithm explores the data to eliminate noisy, irrelevant, redundant data, and simultaneously optimize the classification performance. In this paper, a classification accuracy-based fitness function is proposed by gray-wolf optimizer to find optimal feature subset. Gray-wolf optimizer is a new evolutionary computation technique which mimics the leadership hierarchy and hunting mechanism of gray wolves in nature. The aim of the gray wolf optimization is find optimal regions of the complex search space through the interaction of individuals in the population. Compared with particle swarm optimization (PSP) and Genetic Algorithms (GA) over a set of UCI machine learning data repository, the proposed approach proves better performance in both classification accuracy and feature size reduction. Moreover, the gray wolf optimization approach proves much robustness against initialization in comparison with PSO and GA optimizers.