关键词: 粗糙集, 条件熵, 加权属性重要性, 熵约简

Abstract: Attribute reduction is one of the most important contents of rough set theory, and many of the existing reduction algorithms are often based on attribute importance. If the result of attribute reduction can meet the information of actual need, such as the costs and users’ preference, etc, the theory of reduction will have higher practical value. So, this paper defines the attribute importance with weights based on information entropy, then defines attribute entropy reduction algorithm which is based on the attribute importance with weights. Finally, the experimental results show that, compared with the entropy reduction algorithm based on the attribute importance, the algorithm with weights is more coincident with the actual requirements of users.

Key words: rough set, condition entropy, weighted attribute importance, entropy reduction