关键词: 粗糙集, 相对值条件互信息, 主动学习

Abstract: Since the distribution characteristics of condition attributes in different instances and subjective characteristics they reflect are different，each corresponding to a sample of the real situation in local mapping.In this paper，information theory will be integrated into the rough set algorithm learning process，and learning approach is given by the attribute reduction for decision table.Continuous attributes are handled based on post discretization strategy to balance between the loss of information and discretization efficiency.The definition of conditional mutual information between relative values is given to measure the attribute relevancy in a single sample，and the authors can fully make use of existing data to process incomplete information systems.Even in the lack of prior knowledge，the authors can also apply active learning to learn the new rules and add them to the knowledge base.Thus the application domain of rough set theory is extended.The experimental results on UCI machine learning data sets and analysis of the instances proved that，the algorithm proposed in this paper is reasonable and effective.

Key words: rough set, conditional mutual information between relative values, active learning