On the usefulness of fuzzy SVMs and the extraction of fuzzy rules from SVMs

Abstract

In this paper we reason about the usefulness of two recent trends in fuzzy methods in machine learning. That is, we discuss both fuzzy support vector machines (FSVMs) and the extraction of fuzzy rules from SVMs. First, we show that an FSVM is identical to a special type of SVM. Second, we categorize and analyze existing approaches to obtain fuzzy rules from SVMs. Finally, we question both trends and conclude with more promising alternatives.