关键词: 特征选择, 正域, 大间隔, 邻域粗糙集

Abstract: It is noticeable that the feature selection based on neighborhood rough sets cannot fully measure the relationship between features and samples. This paper proposes a feature selection algorithm based on weighted positive region, by fusing the measure criterion of feature obtained from samples based on large margin. This algorithm comprehensively uses the discernment ability of samples obtained from features and the contribution degree of samples obtained from features. The experimental results based on UCI datasets and high-dimension small-sample datasets show that compared with the traditional single criterion algorithm, the proposed algorithm gets better classification performance, and is particularly useful for handling high-dimension and small-sample datasets.

Key words: feature selection, positive region, large margin, neighborhood rough sets