关键词: 概率粗糙集, 属性约简, 约简异常, 最大决策熵, 信度差别矩阵

Abstract: In order to solve the problem of reduction anomaly in the existing probabilistic rough set models, non-parameterized and parameterized maximum decision entropy measures for attribute reduction were proposed by using the concept of maximum confidence of uncertain object. The monotonicity of the parameterized maximum decision entropy was explained and the relationship between its attribute reduction and other ones was analyzed. The definitions for core and relatively dispensable attributes in the proposed model were also given. Moreover, non-parameterized and parameterized confidence discernibility matrixes were put forward and the difference of classical discernibility matrix and the proposed ones in charactering the uncertain object were discussed. Finally, a case study was given to show the validity of the proposed model.

Key words: probabilistic rough set, attribute reduction, reduction anomaly, maximum decision entropy, confidence discernibility matrix