Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
C4.5: programs for machine learning
C4.5: programs for machine learning
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Principles of data mining
Fuzzy Modeling for Control
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Three objective genetics-based machine learning for linguisitc rule extraction
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook
Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook
IEEE Transactions on Knowledge and Data Engineering
Performance Evaluation of Some Clustering Algorithms and Validity Indices
IEEE Transactions on Pattern Analysis and Machine Intelligence
Supervised fuzzy clustering for the identification of fuzzy classifiers
Pattern Recognition Letters
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Generation of comprehensible decision trees through evolution of training data
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Invariant operators, small samples, and the bias-variance dilemma
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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
Modified Gath-Geva fuzzy clustering for identification of Takagi-Sugeno fuzzy models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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
Takagi-Sugeno fuzzy modeling incorporating input variables selection
IEEE Transactions on Fuzzy Systems
Author's reply [to comments on 'A proposal to improve the accuracy of linguistic modelling']
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Nonlinear control structures based on embedded neural system models
IEEE Transactions on Neural Networks
Fuzzy classifier identification using decision tree and multiobjective evolutionary algorithms
International Journal of Approximate Reasoning
Quality management in GPRS networks with fuzzy case-based reasoning
Knowledge-Based Systems
Constructing accurate fuzzy classifiers: A new adaptive method for rule-weight specification
International Journal of Knowledge-based and Intelligent Engineering Systems
Developing a bioaerosol detector using hybrid genetic fuzzy systems
Engineering Applications of Artificial Intelligence
A hybrid coevolutionary algorithm for designing fuzzy classifiers
Information Sciences: an International Journal
A Framework for Designing a Fuzzy Rule-Based Classifier
ADT '09 Proceedings of the 1st International Conference on Algorithmic Decision Theory
| Hi-index | 0.00 |
This paper studies the identification of fuzzy classifiers and function estimators focusing on improving their interpretability while maintaining their accuracy. Advances of various methods, such as, input variable selection, appropriate initialization algorithms, evolutionary algorithms and simplification techniques are hybridized to form a framework capable of identifying interpretable and accurate fuzzy models (FMs). FMs are initialized by two algorithms. Modified Gath-Geva (MGG) is used for function estimation and C4.5 for classification problems. The initialized FMs go through a three-step GA-based optimization, in which the adequate structure and parameters of FMs are searched. The proposed fitness function makes the favoring of simple FMs possible. Furthermore, the rule base is made more comprehensible by reducing the number of conditions in the rules. The validity of FMs is verified through studying several well-known benchmark problems. The results indicate, that by means of the proposed framework, interpretable, yet accurate FMs are obtained.