Mining Efficient and Interpretable Fuzzy Classifiers from Data with Support Vector Learning

Abtract: The construction of fuzzy rule-based classification systems with both good generalization ability and interpretability is a chalenging issue. The paper aims to present a novel framework for the realization of these important (and many times conflicting) goals simultaneously. The generalization performance is obtained with the adaptation of Support Vector algorithms for the identification of a Support Vector Fuzzy Inference (SVFI) system. The SVFI is a fuzzy inference system that implements the Support Vector network inference and inherits from it the robust learning potential. The construction of the SVFI is based on the algorithms presented in [6]. The contribution of the paper is the development of algorithms for the construction of interpretable rule systems on top of the SVFI system. However, the SVFI rules usually lack interpretability. For this reason, the accurate set of rules is approximated with a simpler interpretable fuzzy system that can present insight to the more important aspects of the data.