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Issue No. 05 - May (2010 vol. 22)
ISSN: 1041-4347
pp: 624-638
Suyun Zhao , Hong Kong Polytechnic University, Hong Kong and Hebei University, Baoding
Eric C.C. Tsang , Hong Kong Polytechnic University, Hong Kong
Degang Chen , North China Electric Power University, Beijing
XiZhao Wang , Hebei University, Baoding
The fuzzy-rough set (FRS) methodology, as a useful tool to handle discernibility and fuzziness, has been widely studied. Some researchers studied on the rough approximation of fuzzy sets, while some others focused on studying one application of FRS: attribute reduction (i.e., feature selection). However, constructing classifier by using FRS, as another application of FRS, has been less studied. In this paper, we build a rule-based classifier by using one generalized FRS model after proposing a new concept named as “consistence degree” which is used as the critical value to keep the discernibility information invariant in the processing of rule induction. First, we generalized the existing FRS to a robust model with respect to misclassification and perturbation by incorporating one controlled threshold into knowledge representation of FRS. Second, we propose a concept named as “consistence degree” and by the strict mathematical reasoning, we show that this concept is reasonable as a critical value to reduce redundant attribute values in database. By employing this concept, we then design a discernibility vector to develop the algorithms of rule induction. The induced rule set can function as a classifier. Finally, the experimental results show that the proposed rule-based classifier is feasible and effective on noisy data.
Knowledge-based systems, fuzzy-rough hybrids, rule-based classifier, IF-THEN rule.

D. Chen, X. Wang, S. Zhao and E. C. Tsang, "Building a Rule-Based Classifier—A Fuzzy-Rough Set Approach," in IEEE Transactions on Knowledge & Data Engineering, vol. 22, no. , pp. 624-638, 2009.
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