Fuzzy system modeling by fuzzy partition and GA hybrid schemes
Fuzzy Sets and Systems
Processing individual fuzzy attributes for fuzzy rule induction
Fuzzy Sets and Systems
A fuzzy neural network for pattern classification and feature selection
Fuzzy Sets and Systems
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Subsethood based adaptive linguistic networks for pattern classification
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A novel approach to feature selection based on analysis of class regions
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A new method for constructing membership functions and fuzzy rulesfrom training examples
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy rule extraction from ID3-type decision trees for real data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An empirical risk functional to improve learning in a neuro-fuzzy classifier
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A fuzzy classifier with ellipsoidal regions
IEEE Transactions on Fuzzy Systems
Implementation of evolutionary fuzzy systems
IEEE Transactions on Fuzzy Systems
GA-fuzzy modeling and classification: complexity and performance
IEEE Transactions on Fuzzy Systems
Compact and transparent fuzzy models and classifiers through iterative complexity reduction
IEEE Transactions on Fuzzy Systems
Complexity and multithreaded implementation analysis of one class-classifiers fuzzy combiner
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
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This paper presents new pruning and learning methods for the fuzzy rule-based classifier. For the simplicity of the model structure, the unnecessary features for each fuzzy rule are eliminated through the iterative pruning algorithm. The quality of the feature is measured by the proposed correctness method, which is defined as the ratio of the fuzzy values for a set of the feature values on the decision region to one for all feature values. For the improvement of the classification performance, the parameters of the proposed classifier are adjusted by using the gradient descent method so that the misclassified feature vectors are correctly re-categorized. Finally, the fuzzy rule-based classifier is tested on two data sets and is found to demonstrate an excellent performance.