Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Logic Minimization Algorithms for VLSI Synthesis
Logic Minimization Algorithms for VLSI Synthesis
The Minimization Problem for Boolean Formulas
FOCS '97 Proceedings of the 38th Annual Symposium on Foundations of Computer Science
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
Fuzzy Systems Engineering: Toward Human-Centric Computing
Fuzzy Systems Engineering: Toward Human-Centric Computing
IEEE Transactions on Knowledge and Data Engineering
Looking for a good fuzzy system interpretability index: An experimental approach
International Journal of Approximate Reasoning
Clustering: A neural network approach
Neural Networks
A Study on Interpretability Conditions for Fuzzy Rule-Based Classifiers
ISDA '09 Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications
IEEE Transactions on Fuzzy Systems - Special section on computing with words
Interpretability assessment of fuzzy knowledge bases: A cointension based approach
International Journal of Approximate Reasoning
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Intelligent Systems, Design and Applications (ISDA 2009)
Rule-based modeling: precision and transparency
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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
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
Tackling outliers in granular box regression
Information Sciences: an International Journal
Information Sciences: an International Journal
Introducing the Discriminative Paraconsistent Machine (DPM)
Information Sciences: an International Journal
Information Sciences: an International Journal
Adaptability, interpretability and rule weights in fuzzy rule-based systems
Information Sciences: an International Journal
Information Sciences: an International Journal
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In computing with words (CWW), knowledge is linguistically represented and has an explicit semantics defined through fuzzy information granules. The linguistic representation, in turn, naturally bears an implicit semantics that belongs to users reading the knowledge base; hence a necessary condition for achieving interpretability requires that implicit and explicit semantics are cointensive. Interpretability is definitely stringent when knowledge must be acquired from data through inductive learning. Therefore, in this paper we propose a methodology for designing interpretable fuzzy models through semantic cointension. We focus our analysis on fuzzy rule-based classifiers (FRBCs), where we observe that rules resemble logical propositions, thus semantic cointension can be partially regarded as the fulfillment of the ''logical view'', i.e. the set of basic logical laws that are required in any logical system. The proposed approach is grounded on the employment of a couple of tools: DCf, which extracts interpretable classification rules from data, and Espresso, that is capable of fast minimization of Boolean propositions. Our research demonstrates that it is possible to design models that exhibit good classification accuracy combined with high interpretability in the sense of semantic cointension. Also, structural parameters that quantify model complexity show that the derived models are also simple enough to be read and understood.