Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Fuzzy Data Mining: Effect of Fuzzy Discretization
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Accuracy Improvements in Linguistic Fuzzy Modeling
Accuracy Improvements in Linguistic Fuzzy Modeling
Classification and Modeling with Linguistic Information Granules: Advanced Approaches to Linguistic Data Mining (Advanced Information Processing)
Preface: Special Issue on Genetic Fuzzy Systems and the Interpretability--Accuracy Trade-off
International Journal of Approximate Reasoning
A proposal on reasoning methods in fuzzy rule-based classification systems
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
Combinatorial rule explosion eliminated by a fuzzy rule configuration
IEEE Transactions on Fuzzy Systems
Effect of rule weights in fuzzy rule-based classification systems
IEEE Transactions on Fuzzy Systems
Rule Weight Specification in Fuzzy Rule-Based Classification Systems
IEEE Transactions on Fuzzy Systems
Selecting fuzzy if-then rules for classification problems using genetic algorithms
IEEE Transactions on Fuzzy Systems
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In this paper, a genetic algorithm-based search method, which builds ideal rule set for fuzzy rule-based classification systems FRBCSs, is developed. In FRBCSs, ideal rule set means a set of rules which ensure high classification accuracy with small rule count and small rule length. The related studies in the literature point out that rule set grows exponentially with input attribute count. This growth complicates the searching process and lowers the success rate. Through the proposed method, successive results are obtained for datasets with large input attribute counts using a special coding technique. The proposed method is tested for various datasets and results are compared against the method which uses candidate rule set.