Genetic algorithms for fuzzy controllers
AI Expert
A learning process for fuzzy control rules using genetic algorithms
Fuzzy Sets and Systems
Towards neuro-linguistic modeling: constraints for optimization of membership functions
Fuzzy Sets and Systems
A genetic-algorithm-based method for tuning fuzzy logic controllers
Fuzzy Sets and Systems
Three objective genetics-based machine learning for linguisitc rule extraction
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Accuracy Improvements in Linguistic Fuzzy Modeling
Accuracy Improvements in Linguistic Fuzzy Modeling
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on soft computing for information mining
International Journal of Approximate Reasoning
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
A Pareto-based multi-objective evolutionary approach to the identification of Mamdani fuzzy systems
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Comments on "Distinguishability quantification of fuzzy sets"
Information Sciences: an International Journal
Application of Fuzzy Logic to Approximate Reasoning Using Linguistic Synthesis
IEEE Transactions on Computers
Fuzzy classifier identification using decision tree and multiobjective evolutionary algorithms
International Journal of Approximate Reasoning
International Journal of Intelligent Systems
Interpretability constraints for fuzzy information granulation
Information Sciences: an International Journal
KEEL: a software tool to assess evolutionary algorithms for data mining problems
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary and Metaheuristics based Data Mining (EMBDM); Guest Editors: José A. Gámez, María J. del Jesús, José M. Puerta
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Genetic Fuzzy Systems: Recent Developments and Future Directions; Guest editors: Jorge Casillas, Brian Carse
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Genetic Fuzzy Systems: Recent Developments and Future Directions; Guest editors: Jorge Casillas, Brian Carse
Handbook of Parametric and Nonparametric Statistical Procedures
Handbook of Parametric and Nonparametric Statistical Procedures
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Looking for a good fuzzy system interpretability index: An experimental approach
International Journal of Approximate Reasoning
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Similarity measures in fuzzy rule base simplification
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
Constraining the optimization of a fuzzy logic controller using anenhanced genetic algorithm
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
Constructing fuzzy models with linguistic integrity from numerical data-AFRELI algorithm
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Selecting fuzzy if-then rules for classification problems using genetic algorithms
IEEE Transactions on Fuzzy Systems
Interpretability assessment of fuzzy knowledge bases: A cointension based approach
International Journal of Approximate Reasoning
Intelligent data analysis and model interpretation with spectral analysis fuzzy symbolic modeling
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures
Information Sciences: an International Journal
Editorial: Special issue on interpretable fuzzy systems
Information Sciences: an International Journal
Design of fuzzy rule-based classifiers with semantic cointension
Information Sciences: an International Journal
Learning interpretable fuzzy inference systems with FisPro
Information Sciences: an International Journal
Multi-objective evolutionary fuzzy systems
WILF'11 Proceedings of the 9th international conference on Fuzzy logic and applications
A double axis classification of interpretability measures for linguistic fuzzy rule-based systems
WILF'11 Proceedings of the 9th international conference on Fuzzy logic and applications
An efficient multi-objective evolutionary fuzzy system for regression problems
International Journal of Approximate Reasoning
Adaptability, interpretability and rule weights in fuzzy rule-based systems
Information Sciences: an International Journal
A fuzzy system index to preserve interpretability in deep tuning of fuzzy rule based classifiers
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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In this paper, we propose an index that helps preserve the semantic interpretability of linguistic fuzzy models while a tuning of the membership functions (MFs) is performed. The proposed index is the aggregation of three metrics that preserve the original meanings of the MFs as much as possiblewhile a tuning of their definition parameters is performed. Additionally, rule-selection mechanisms can be used to reduce the model complexity, which involves another important interpretability aspect. To this end, we propose a postprocessing multiobjective evolutionary algorithm that performs rule selection and tuning of fuzzy-rule-based systems with three objectives: accuracy, semantic interpretability maximization, and complexity minimization. We tested our approach on nine real-world regression datasets. In order to analyze the interaction between the fuzzy-rule-selection approach and the tuning approach, these are also individually proved in a multiobjective framework and compared with their respective single-objective counterparts. We compared the different approaches by applying nonparametric statistical tests for pairwise and multiple comparisons, taking into consideration three representative points fromthe obtained Pareto fronts in the case of the multiobjective-based approaches. Results confirm the effectiveness of our approach, and a wide range of solutions is obtained, which are not only more interpretable but are also more accurate.