Opposition-based Grey Wolf Optimization Approaches for Feature Selection
Zhi Cao, Hongmei Chen · 2021 16th International Conference on Intelligent Systems and Knowledge Engineering (ISKE) · 2021
Feature selection is an approach to select the important and key feature subset from a large number of redundant features, which can effectively reduce the computational cost and storage cost in the data processing process. Therefore, feature selection is widely used in data mining and pattern recognition. The grey Wolf optimization algorithm can solve the problem of feature selection by simulating the predation behavior of gray wolves to obtain the optimal feature subset step by step. In this paper, we propose an opposition based learning grey Wolf optimization algorithm(OGWO), which uses opposition-based learning to initialize and update the population to improve the exploration ability of the population. In order to avoid the local optimum, a nonlinear parameter adjustment strategy is used to update the important parameters of the grey Wolf optimization algorithm. Experiments on 12 datasets demonstrate the effectiveness of the proposed method. Experimental results show that OGWO has better classification performance compared with other swarm intelligence algorithms.