UNCERTAINTY MEASURE OF KNOWLEDGE AND ROUGH SET BASED ON MAXIMAL CONSISTENT BLOCK TECHNIQUE

摘要：

In incomplete information systems, similarity measures or tolerance relations replace indiscernible relations, and the corresponding similarity or tolerance classes form coverage instead of classification of Universe.On the other hand, without satisfying the properties of transference and symmetry, there may have misjudgments in tolerance or similarity classes.Therefore, it is necessary to study roughness of knowledge and rough set based on suitable knowledge granularity in incomplete information systems.The present paper proposes a method to measure uncertain knowledge and rough set according to maximal consistent block technique, which provides the basic knowledge granulation from the similarity classes without changing the relevant model.Moreover, some new definitions about the roughness of knowledge and rough set are also discussed in the proposed method.

关键词： Rough set theory Maximal consistent block Uncertainty measure Rough entropy

作者: YU-SHENG CHENG YOU-SHENG ZHANG XUE-GANG HU YOU-ZHI ZHANG

作者单位: School of Computer Science, Anqing Teachers College, Anqing 246011,China;School of Computer Science, School of Computer Science, Hefei University of Technology, Hefei 230009,China School of Computer Science, Anqing Teachers College, Anqing 246011,China

会议类型: 国际会议

会议名称: 2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

会议地点: 香港

会议语种:英文

页码: 3069-3074

在线出版日期: 2007-08-19（万方平台首次上网日期，不代表论文的发表时间）