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Improving Generalization of Fuzzy IF--THEN Rules by Maximizing Fuzzy Entropy

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2 Author(s)
Xi-Zhao Wang ; Dept. of Math. & Comput. Sci., Hebei Univ., Baoding ; Chun-Ru Dong

When fuzzy IF-THEN rules initially extracted from data have not a satisfying performance, we consider that the rules require refinement. Distinct from most existing rule-refinement approaches that are based on the further reduction of training error, this paper proposes a new rule-refinement scheme that is based on the maximization of fuzzy entropy on the training set. The new scheme, which is realized by solving a quadratic programming problem, is expected to have the advantages of improving the generalization capability of initial fuzzy IF-THEN rules and simultaneously overcoming the overfitting of refinement. Experimental results on a number of selected databases demonstrate the expected improvement of generalization capability and the prevention of overfitting by a comparison of both training and testing accuracy before and after the refinement.

Published in:

Fuzzy Systems, IEEE Transactions on  (Volume:17 ,  Issue: 3 )