Data-driven design of fuzzy classification rules with semantic cointension

A key feature for machine intelligence is the ability of learning knowledge from past experiences. Furthermore, in a human-centric environment, the acquired knowledge must fulfill comprehensibility requirements so as to be shared by human users. In literature, several approaches have been proposed to acquire comprehensible knowledge from data by preserving a number of interpretability constraints, especially for Fuzzy Rule-Based Classifiers (FRBCs). As a general result, accuracy and interpretability emerge as conflicting features, so that a tradeoff is often required. In consequence of this tradeoff, the resulting FRBCs are provided with a knowledge base expressed in natural language but, as a matter of fact, the semantics embedded by the linguistic structures might not be cointensive with the explicit semantics defined in the knowledge base. As an alternative approach, in this paper we propose a technique to design FRBCs from data with the specific aim of maximizing interpretability in the sense of semantic cointension. The most important result of this approach is to control cointension so as to select models that possess knowledge bases that users can understand on the basis of their natural language description. This enables the use of the FRBC in a human-centric environment. Experimental sessions are performed on benchmark classification problems to show the effectiveness of the proposed approach.