Rough Rule Extracting From Various Conditions: Incremental and Approximate Approaches for Inconsistent Data

Authors:
Yong Liu;Congfu Xu;Qiong Zhang;Yunhe Pan
Affiliations:
(Correspd.) State Key Lab. of Industrial Control Technology, Zhejiang University, China Institute of Advanced Process Control, Zhejiang University, China. E-mail: yongliu@iipc.zju.edu.cn;College of Computer Science, Zhejiang University, China;College of Computer Science, Zhejiang University, China;College of Computer Science, Zhejiang University, China
Venue:
Fundamenta Informaticae
Year:
2008

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Abstract

Rough rule extraction refers to the rule induction method by using rough set theory. Although rough set theory is a powerful mathematical tool in dealing with vagueness and uncertainty in data sets, it is lack of effective rule extracting approach under complex conditions. This paper proposes several algorithms to perform rough rule extraction from data sets with different properties. Firstly, in order to obtain uncertainty rules from inconsistent data, we introduce the concept of confidence factor into the rule extracting process. Then, an improved incremental rule extracting algorithm is proposed based on the analysis of the incremental data categories. Finally, above algorithms are further extended to perform approximate rule extraction from huge data sets. Preliminary experiment results are encouraging.