关键词: 特征选择, 邻域粗糙集, 邻域互信息, 三支决策, 集成学习

Abstract: Feature selection is a very important preprocessing step in machine learning, and neighborhood mutual information is an effective measure to deal with continuous or discrete features. However, existing feature selection methods based on neighborhood mutual information usually employ the heuristic greedy strategy to find the optimal reduct, so it is difficult to guarantee the quality of feature subsets. Based on the theory of three-way decision, a three-way neighborhood mutual information feature selection method（NMI-TWD） is proposed. Three potential candidate feature subsets with diversity are first generated in order to obtain feature subsets with higher quality. Then three-way co-decision model is developed to further improve the performance through ensembling the three obtained feature subsets. The extensive experiments conducted on UCI data sets show that the proposed method achieves better reducts and classification performance in comparison with other representative methods, validating its effectiveness.

Key words: feature selection, neighborhood rough set, neighborhood mutual information, three-way decision, ensemble learning