关键词: 缺失数据, 粗糙集, 互补信息熵, 特征选择

Abstract: Nowadays, most of the data are dynamically changing in daily life. As the data grow dynamically, many features contain missing data. How to deal with dynamic feature sets with missing data sets becomes an urgent problem. So, based on the rough sets theory, this paper updates the mechanism of complementary information entropy when the dimension of data set with missing data increases. Then, an incremental feature selection algorithm of missing data dimension is proposed. Finally, the feasibility and efficiency of the algorithm are verified by experiments.

Key words: missing data, rough sets, complementary information entropy, feature selection