Fuzzy rule extraction from support vector machines

This paper proposes a fuzzy rule extraction method from support vector machines. Support vector machines (SVM) are learning systems based on statistical learning theory that have been successfully applied to a wide variety of application. However, SVM are "black box" models, that is, they generate a solution with linear combination of kernel functions which has a quite difficult interpretation. Methods for rule extraction from trained SVM have already been proposed, however, the rules generated by these methods have, in their antecedents, intervals or functions. This format decreases the interpretability of the generated rules and jeopardizes the knowledge extraction capability. Hence, to increase the linguistic interpretability of the generated rules, we propose in this paper a methodology for extracting fuzzy rules from a trained SVM, where the rule's antecedents are associated with fuzzy sets.

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