Journal
IEEE TRANSACTIONS ON FUZZY SYSTEMS
Volume 17, Issue 3, Pages 556-567Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TFUZZ.2008.924342
Keywords
Classification; fuzzy entropy; fuzzy IF-THEN rules; maximum entropy principle; parametric fuzzy IF-THEN rules; rule-based reasoning
Funding
- National Nature Science Foundation of China [60473045]
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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.
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