INFORMATION-PRESERVING RULE INDUCTION BY USING GENERALIZED FUZZY-ROUGH TECHNIQUE
In this paper, we build a rule-based classifier by using generalized FRS with variable precision after proposing a new concept named consistence degree which is used as the critical value to keep the information invariant in the processing of rule induction. First, we improve the existing FRS by incorporating one controlled threshold into knowledge representation of fuzzy rough sets so that fuzzy rough sets become a robust model. Second, we describe some concepts of attribute-value reduction. The key idea of attribute-value reduction is to keep the consistence degree, i.e. fuzzy lower approximation value of certain decision class invariant before and after reduction. Third, a set of rules which covers all the objects in the original dataset can be obtained after the description of rule representation system in fuzzy decision table. Finally, the experimental results show that the proposed rule-based classifier is feasible, and effective on noisy data. The main contribution of this paper is that the rule induction method is well combined with knowledge representation of fuzzy rough sets by using fuzzy lower approximation value.
Fuzzy rough sets variable precision classification IF-THEN rule
ERIC C.C.TSANG SU-YUN ZHAO
Department of computing, The Hong Kong Polytechnic University, Hung Horn, Kowloon, Hong Kong Department of computing, The Hong Kong Polytechnic University, Hung Horn, Kowloon, Hong Kong Machine
国际会议
2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)
昆明
英文
1795-1800
2008-07-12（万方平台首次上网日期，不代表论文的发表时间）