An Interpretable Fuzzy Classifier Learned Through Soft-Margin Minimization with Transparent Fuzzy Sets

This paper proposes an interpretable fuzzy classifier learned through soft-margin minimization (IFC-SMM) with the consideration of fuzzy set transparency. The rules of the IFC-SMM are built through a firing-strength-based clustering method. Parameters in the IFC-SMM are learned through SMM to endow the fuzzy classifier (FC) with high generalization ability. Based on the SMM, the antecedent and consequent parameters in the built rules are learned through the gradient descent algorithm and support vector machine (SVM), respectively. To improve the interpretability of the IFC-SMM, two constraints imposed on fuzzy set transparency are introduced into the SMM in the tuning of the antecedent parameters. Experimental results on real classification problems with comparisons with different FCs show the characteristics of the IFC-SMM.