Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Extracting Interpretable Fuzzy Rules from RBF Networks
Neural Processing Letters
Expert guided integration of induced knowledge into a fuzzy knowledge base
Soft Computing - A Fusion of Foundations, Methodologies and Applications
International Journal of Approximate Reasoning
Fuzzy classifier identification using decision tree and multiobjective evolutionary algorithms
International Journal of Approximate Reasoning
Is there a need for fuzzy logic?
Information Sciences: an International Journal
International Journal of Intelligent Systems
Interpretability constraints for fuzzy information granulation
Information Sciences: an International Journal
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Genetic Fuzzy Systems: Recent Developments and Future Directions; Guest editors: Jorge Casillas, Brian Carse
International Journal of Approximate Reasoning
IEEE Transactions on Knowledge and Data Engineering
Looking for a good fuzzy system interpretability index: An experimental approach
International Journal of Approximate Reasoning
IEEE Transactions on Fuzzy Systems - Special section on computing with words
Toward Human Level Machine Intelligence - Is It Achievable? The Need for a Paradigm Shift
IEEE Computational Intelligence Magazine
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Similarity measures in fuzzy rule base simplification
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
Fuzzy logic = computing with words
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement
IEEE Transactions on Fuzzy Systems
Constructing fuzzy models with linguistic integrity from numerical data-AFRELI algorithm
IEEE Transactions on Fuzzy Systems
Compact and transparent fuzzy models and classifiers through iterative complexity reduction
IEEE Transactions on Fuzzy Systems
Fuzzy CoCo: a cooperative-coevolutionary approach to fuzzy modeling
IEEE Transactions on Fuzzy Systems
Generating an interpretable family of fuzzy partitions from data
IEEE Transactions on Fuzzy Systems
Logic Minimization as an Efficient Means of Fuzzy Structure Discovery
IEEE Transactions on Fuzzy Systems
An enhanced two-level Boolean synthesis methodology for fuzzy rules minimization
IEEE Transactions on Fuzzy Systems
A neuro-fuzzy network to generate human-understandable knowledge from data
Cognitive Systems Research
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
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 empirical study on interpretability indexes through multi-objective evolutionary algorithms
WILF'11 Proceedings of the 9th international conference on Fuzzy logic and applications
Interpretable fuzzy modeling for decision support in IgA nephropathy
WILF'11 Proceedings of the 9th international conference on Fuzzy logic and applications
Generating understandable and accurate fuzzy rule-based systems in a java environment
WILF'11 Proceedings of the 9th international conference on Fuzzy logic and applications
A genetic design of linguistic terms for fuzzy rule based classifiers
International Journal of Approximate Reasoning
Towards linguistic descriptions of phenomena
International Journal of Approximate Reasoning
Granular computing for relational data classification
Journal of Intelligent Information Systems
Adaptability, interpretability and rule weights in fuzzy rule-based systems
Information Sciences: an International Journal
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Computing with words (CWW) relies on linguistic representation of knowledge that is processed by operating at the semantical level defined through fuzzy sets. Linguistic representation of knowledge is a major issue when fuzzy rule based models are acquired from data by some form of empirical learning. Indeed, these models are often requested to exhibit interpretability, which is normally evaluated in terms of structural features, such as rule complexity, properties on fuzzy sets and partitions. In this paper we propose a different approach for evaluating interpretability that is based on the notion of cointension. The interpretability of a fuzzy rule-based model is measured in terms of cointension degree between the explicit semantics, defined by the formal parameter settings of the model, and the implicit semantics conveyed to the reader by the linguistic representation of knowledge. Implicit semantics calls for a representation of user's knowledge which is difficult to externalise. Nevertheless, we identify a set of properties - which we call ''logical view'' - that is expected to hold in the implicit semantics and is used in our approach to evaluate the cointension between explicit and implicit semantics. In practice, a new fuzzy rule base is obtained by minimising the fuzzy rule base through logical properties. Semantic comparison is made by evaluating the performances of the two rule bases, which are supposed to be similar when the two semantics are almost equivalent. If this is the case, we deduce that the logical view is applicable to the model, which can be tagged as interpretable from the cointension viewpoint. These ideas are then used to define a strategy for assessing interpretability of fuzzy rule-based classifiers (FRBCs). The strategy has been evaluated on a set of pre-existent FRBCs, acquired by different learning processes from a well-known benchmark dataset. Our analysis highlighted that some of them are not cointensive with user's knowledge, hence their linguistic representation is not appropriate, even though they can be tagged as interpretable from a structural point of view.