关键词: 特征选择, 大间隔, 对称不确定性, 局部随机子空间

Abstract: Two kinds of feature selection algorithms are further studied, i.e., the characteristic of large margin is the similarity between samples and the entropy is the correlation between features, an effective feature selection algorithm via fusing large margin and information entropy is proposed. The features are ranked by employing the algorithm of Relief, and the ranked feature list is partitioned into a few sections. Based on the heuristic factor of symmetric uncertainty, the feature subset in each local random subspace is obtained by setting the sampling rate of each section. The final feature subset is obtained by merging all feature subsets. Experimental results show that the proposed algorithm is superior to several feature selection algorithms.

Key words: feature selection, large margin, symmetric uncertainty, local random subspace