Reduced error specialization based on the information content of rule set

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.

作者: Dan Hu Xianchuan Yu Yuanfu Feng

作者单位: Beijing Normal University College of Information Science and Technology Beijing 100875, P.R.China Beijing Union University Basic Courses Department Beijing 100101, P.R.China

会议类型: 国际会议

会议名称: 2011 Eighth International Conference on Fuzzy System and Knowledge Discovery(第八届模糊系统与知识发现国际会议 FSKD 2011)

会议地点: 上海

会议语种:英文

页码: 1097-1101

在线出版日期: 2011-07-26（万方平台首次上网日期，不代表论文的发表时间）