关键词: 不完备数据, 粗糙集, 特征选择, 属性约简

Abstract: The feature selection of multi-label data is considered as an important research issue in machine learning and data mining. At present, most feature selection works of multi-label deal with the complete data. However, in many applications, the data are continuous, because of the high diagnosis cost, privacy protection or other factors, resulting in the incompleteness. To address this issue, a feature selection algorithm in multi-label incomplete data is proposed. Neighborhood rough set model is applied for feature selection in multi-label incomplete data, and then the neighborhood granularities of the multi-label incomplete data are computed by the neighborhood threshold. The criterion of feature significance is developed based on neighborhood granularities. On this basis, the feature selection algorithm is designed for multi-label incomplete data. Finally, the effectiveness and feasibility of the proposed algorithm are verified by the experimental results on the Mulan dataset.

Key words: incomplete data, rough sets, feature selection, attribute reduction