关键词: 引力搜索, 特征选择, 自适应, 分类算法, 混合优化

Abstract: To overcome the premature search convergence and the stagnation situation, in this paper, a novel algorithm based on hybrid self-adaptive gravitational search algorithm HSA-GSA is proposed. It can not only maximize the classification accuracy but also select the best subset of features from the data samples. The proposed algorithm combines two solution update strategies to search the solutions, and introduces population reduction method, which effectively balances the exploration search and exploitation search. Moreover, self-adaptive control parameters are designed to reduce the influence on the algorithm due to manually setting. The experimental results on seven practical datasets show that the proposed algorithm HSA has better comprehensive performance with respect to classification accuracy, size of feature subsets and efficiency than original GSA algorithm and similar algorithms. It also proves that the effectiveness of the proposed algorithm.

Key words: gravitational search algorithm, feature selection, self-adaptive, classification method, hybrid optimization