DocumentCode :
1934919
Title :
Rule Induction Based on Fuzzy Rough Sets
Author :
Tsang, Eric C C ; Zhao, Su-yun ; Lee, John W T
Author_Institution :
Hong Kong Polytech. Univ., Kowloon
Volume :
5
fYear :
2007
fDate :
19-22 Aug. 2007
Firstpage :
3028
Lastpage :
3033
Abstract :
In this paper, we propose one method of rule induction based on fuzzy rough set. First, the consistence degree is proposed as the basic concept to induce rules based on fuzzy rough sets. The concepts of rule induction, such as value reduct, reduct rule and so on, are then proposed based on the definition of consistence degree. Second, a discernibility array is constructed, and then an algorithm to find the reduct rule using the discernibility array is designed. Finally, the numerical experimental results demonstrate that the method of rule induction proposed in this paper is feasible. The key idea of this paper is that the value reduct (i.e. reduct rule) keeps the consistence degree invariant. The main contribution of this paper is introduction of rule induction based on fuzzy rough sets using the concept of fuzzy lower and upper approximation.
Keywords :
fuzzy set theory; inference mechanisms; knowledge based systems; rough set theory; consistence degree; discernibility array; fuzzy approximation; fuzzy rough set; reduct rule; rule induction; value reduct; Algorithm design and analysis; Approximation algorithms; Computer science; Cybernetics; Fuzzy sets; Machine learning; Mathematical model; Mathematics; Rough sets; Set theory; Discernibility array; Fuzzy rough set; Rule induction; Triangular norm;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics, 2007 International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-0973-0
Electronic_ISBN :
978-1-4244-0973-0
Type :
conf
DOI :
10.1109/ICMLC.2007.4370667
Filename :
4370667
Link To Document :
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