Fuzzy Rules Extraction from Support Vector Machines for Multi-class Classification with Feature Selection

Although Support Vector Machines (SVMs) have been successfully applied to many problems, they are considered "black box models". Some methods have been developed to reduce this limitation, among them the FREx_SVM, which extracts fuzzy rules from trained SVMs for multi-class problems. This work deals with an extension to the FREx_SVM method, including a wrapper feature subset selection algorithm for SVMs. The method was evaluated in four benchmark databases. Results show that the proposed extension improves the original FREX_SVM, providing better rule coverage and a lower number of rules, which is a considerable gain in terms of interpretability.