A Study on Interpretability Conditions for Fuzzy Rule-Based Classifiers

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 cointension. 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.