A Study on Interpretability Conditions for Fuzzy Rule-Based Classifiers

Abstract

Interpretability represents the most important driving force behind the implementation of fuzzy logic-based systems. It can be directly related to the system’s knowledge base, with reference to the human user’s easiness experienced while reading and understanding the embedded pieces of information. In this paper, we present a preliminary study on interpretability conditions for fuzzy rule-based classifiers on the basis of an innovative approach that relies on the concept of semantic cointesion. The approach adopted in this study consists in analysing the components of a fuzzy classifiers so that inference is carried out with the respect of logical properties. As a result, we derive some sufficient conditions and basic requirements to be verified by a fuzzy classifier in order to be tagged as interpretable in the semantic sense.