文献类型：会议论文

英文题名：Reduced error specialization based on the information content of rule set

作者：Hu, Dan[1]; Yu, Xianchuan[1]; Feng, Yuanfu[2]

第一作者：Hu, Dan

通讯作者：Hu, D.

机构：[1] Beijing Normal University, College of Information Science and Technology, Beijing 100875, China; [2] Beijing Union University, Basic Courses Department, Beijing 100101, China

第一机构：Beijing Normal University, College of Information Science and Technology, Beijing 100875, China

会议论文集：Proceedings - 2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011

会议日期：July 26, 2011 - July 28, 2011

会议地点：Shanghai, China

语种：英文

外文关键词：Fuzzy systems

摘要：Except for over-fitting, excessive generalization should lead to high error rate of the learnt rule set, which is seldom discussed by literatures. When excessive generalization is occurred, the rule set will give multiple classification for a particular instance. The errors caused by generalization actually result in the increased inner conflict of the generalized rule set. In this paper, the inner conflict of rule set is defined based on the expanded knowledge of rules and a novel algorithm named RES(reduced error specialization) is proposed for the error rate reduction of rule sets. The best merit of RES is that it can eliminate the inner conflict of a rule set completely while the unknown knowledge of the rule set is unchanged. This fact will guarantee the error rate of the rule set on every test data will be determinedly reduced. ? 2011 IEEE.