Elements of information theory
Elements of information theory
Induction of fuzzy rules and membership functions from training examples
Fuzzy Sets and Systems
Learning fuzzy knowledge from training examples
Proceedings of the seventh international conference on Information and knowledge management
Advanced Fuzzy Systems Design and Applications
Advanced Fuzzy Systems Design and Applications
Cluster ensembles: a knowledge reuse framework for combining partitionings
Eighteenth national conference on Artificial intelligence
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Influential Rule Search Scheme (IRSS)-A New Fuzzy Pattern Classifier
IEEE Transactions on Knowledge and Data Engineering
Adaptive crossover and mutation in genetic algorithms based on clustering technique
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Generating fuzzy membership function with self-organizing feature map
Pattern Recognition Letters
Fuzzy Classifier Design
A new method for constructing membership functions and fuzzy rulesfrom training examples
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy relational classifier trained by fuzzy clustering
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
How good are fuzzy If-Then classifiers?
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An efficient fuzzy classifier with feature selection based on fuzzyentropy
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
Adaptive fuzzy rule-based classification systems
IEEE Transactions on Fuzzy Systems
Implementation of evolutionary fuzzy systems
IEEE Transactions on Fuzzy Systems
Convex-set-based fuzzy clustering
IEEE Transactions on Fuzzy Systems
Self-organized fuzzy system generation from training examples
IEEE Transactions on Fuzzy Systems
Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement
IEEE Transactions on Fuzzy Systems
Genetic algorithm for the design of a class of fuzzy controllers: an alternative approach
IEEE Transactions on Fuzzy Systems
Effect of rule weights in fuzzy rule-based classification systems
IEEE Transactions on Fuzzy Systems
Generating the knowledge base of a fuzzy rule-based system by the genetic learning of the data base
IEEE Transactions on Fuzzy Systems
Improvements and critique on Sugeno's and Yasukawa's qualitative modeling
IEEE Transactions on Fuzzy Systems
Rule Weight Specification in Fuzzy Rule-Based Classification Systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
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In this paper, a framework for automatic generation of fuzzy membership functions and fuzzy rules from training data is proposed. The main focus of this paper is designing fuzzy if-then classifiers; however the proposed method can be employed in designing a wide range of fuzzy system applications. After the fuzzy membership functions are modeled by their supports, an optimization technique, based on a multi-objective real coded genetic algorithm with adaptive cross over and mutation probabilities, is implemented to find near optimal supports. Employing interpretability constraint in parameter representation and encoding, we ensure that the generated fuzzy membership function does have a semantic meaning. The fitness function of the genetic algorithm, which estimates the quality of the generated membership functions, consists of two elements: (i) the Shannon entropy and mutual information measures to measure diversity of the data distribution in a hypercube; and (ii) the number of generated fuzzy rules addressing the measure of compactness of the fuzzy system. Finally membership functions are tuned to yield optimal classifier hypercubes, which represent the predictivity and discriminating power of the classifier. Fuzzy rules of the classifier are derived from the optimal hypercubes. Using the proposed approach to designing fuzzy if-then classifiers, we are also able to evaluate the generated membership functions and compare the results with that of other techniques which have been previously reported in the literature.Using the experimental result, we show that the proposed approach outperforms other techniques in low resolutions. It means that theproposed approach can achieve satisfying result with lower complexity.