Learning consistent, complete and compact sets of fuzzy rules in conjunctive normal form for regression problems |
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| Authors: | Jorge Casillas Pedro Martínez Alicia D Benítez |
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| Affiliation: | (1) Department of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain |
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| Abstract: | When a flexible fuzzy rule structure such as those with antecedent in conjunctive normal form is used, the interpretability
of the obtained fuzzy model is significantly improved. However, some important problems appear related to the interaction
among this set of rules. Indeed, it is relatively easy to get inconsistencies, lack of completeness, redundancies, etc. Generally,
these properties are ignored or mildly faced. This paper, however, focuses on the design of a multiobjective genetic algorithm
that properly considers all these properties thus ensuring an effective search space exploration and generation of highly
legible and accurate fuzzy models.
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| Keywords: | Genetic fuzzy systems Regression problems Multiobjective optimization Flexible fuzzy rules Interpretability constrains |
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