Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Preface: Special Issue on Genetic Fuzzy Systems and the Interpretability--Accuracy Trade-off
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
A proposed method for learning rule weights in fuzzy rule-based classification systems
Fuzzy Sets and Systems
International Journal of Intelligent Systems
Interpretability constraints for fuzzy information granulation
Information Sciences: an International Journal
IEEE Transactions on Knowledge and Data Engineering
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
IEEE Transactions on Fuzzy Systems - Special section on computing with words
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
Rule Weight Specification in Fuzzy Rule-Based Classification Systems
IEEE Transactions on Fuzzy Systems
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Following the successful applications of the fuzzy models in various application domains, the issue of automatic generation of Fuzzy Rule Based Systems FRBSs from observational data was widely studied in the literature and several approaches have been proposed. Most approaches were designed to search for the best accuracy of the generated model, neglecting the interpretability of FRBSs, which is commonly recognized as one of main reasons of the success of fuzzy linguistic models. To fill this gap, a current hot issue in linguistic fuzzy modelling area is the search for a good accuracy-interpretability trade-off. At present, despite the work done, the definition of interpretability is rather problematic. In fact there is still not an universal index widely accepted. This is mainly because the understanding of fuzzy systems is a subjective task that strongly depends on the background of the person who makes the assessment. In consequence an effective way consists of proposing a fuzzy system index instead of numerical ones. In this paper, we give our contribution proposing a fuzzy system as index to measure both fuzzy rule and set levels complexity of the system. At best of our knowledge there are not indexes to preserve interpretability of a FRBS when it is deep tuned, to this end a new fuzzy system index is formulated and an implementation is presented. To show how our fuzzy system index could be used for interpretability preservation, it is integrated in a classical Multi-Objective Evolutionary Algorithm MOEA and its results are presented through six comparative examples based on well-known data sets in the pattern classification field.