Genetic-algorithm-based approaches to classification problems
Fuzzy evolutionary computation
Fuzzy Theory Systems: Techniques and Applications
Fuzzy Theory Systems: Techniques and Applications
Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art
Evolutionary Computation
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
A proposal on reasoning methods in fuzzy rule-based classification systems
International Journal of Approximate Reasoning
Rule-based modeling: precision and transparency
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Similarity measures in fuzzy rule base simplification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Simplifying fuzzy rule-based models using orthogonal transformationmethods
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On generating FC3 fuzzy rule systems from data usingevolution strategies
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Semantic constraints for membership function optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Improving the interpretability of TSK fuzzy models by combining global learning and local learning
IEEE Transactions on Fuzzy Systems
Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement
IEEE Transactions on Fuzzy Systems
GA-fuzzy modeling and classification: complexity and performance
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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We formulate linguistic rule extraction as a three-objective combinatorial optimization problem. Three objectives are to maximize the performance of an extracted rule set, to minimize the number of extracted rules, and to minimize the total length of extracted rules. The second and third objectives are related to comprehensibility of the extracted rule set. We describe and compare two genetic-algorithm-based approaches for finding nondominated rule sets with respect to the three objectives of our linguistic rule extraction problem. One approach is rule selection where a small number of linguistic rules are selected from prespecified candidate rules. The other is genetics-based machine learning where rule sets are evolved by generating new rules from existing ones using genetic operations.