A hybrid coevolutionary algorithm for designing fuzzy classifiers
Information Sciences: an International Journal
A Three-Objective Evolutionary Approach to Generate Mamdani Fuzzy Rule-Based Systems
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Looking for a good fuzzy system interpretability index: An experimental approach
International Journal of Approximate Reasoning
Information Sciences: an International Journal
IEEE Transactions on Fuzzy Systems
A dynamically constrained multiobjective genetic fuzzy system for regression problems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems - Special section on computing with words
On reducing computational overhead in multi-objective genetic Takagi-Sugeno fuzzy systems
Applied Soft Computing
Interpretability assessment of fuzzy knowledge bases: A cointension based approach
International Journal of Approximate Reasoning
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
International Journal of Approximate Reasoning
Identification of transparent, compact, accurate and reliable linguistic fuzzy models
Information Sciences: an International Journal
Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures
Information Sciences: an International Journal
Design of fuzzy rule-based classifiers with semantic cointension
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
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Context adaptation (CA) based on evolutionary algorithms is certainly a promising approach to the development of fuzzy rule-based systems (FRBSs). In CA, a context-free model is instantiated to a context-adapted FRBS so as to increase accuracy. A typical requirement in CA is that the context-adapted system maintains the same interpretability as the context-free model, a challenging constraint given that accuracy and interpretability are often conflicting objectives. Furthermore, interpretability is difficult to quantify because of its very nature of being a qualitative concept. In this paper, we first introduce a novel index based on fuzzy ordering relations in order to provide a measure of interpretability. Then, we use the proposed index and the mean square error as goals of a multi-objective evolutionary algorithm aimed at generating a set of Pareto-optimum context-adapted Mamdani-type FRBSs with different trade-offs between accuracy and interpretability. CA is obtained through the use of specifically designed operators that adjust the universe of the input and output variables, and modify the core, the support and the shape of fuzzy sets characterizing the partitions of these universes. Finally, we show results obtained by using our approach on synthetic and real data sets.