Quest for Interpretability-Accuracy Trade-off Supported by Fingrams into the Fuzzy Modeling Tool GUAJE

Understand the behavior of Fuzzy Rule-based Systems (FRBSs) at inference level is a complex task that allows the designer to produce simpler and powerful systems. The fuzzy inference-grams –known as fingrams– establish a novel and mighty tool for understanding the structure and behavior of fuzzy systems. Fingrams represent FRBSs as social networks made of nodes representing fuzzy rules and edges representing the degree of interaction between pairs of rules at inference level (no edge means no significant interaction). We can analyze fingrams obtaining helpful information such as detecting potential conflicts between rules, unused rules and redundant ones. This paper introduces a new module for fingram generation and analysis included in the free software tool GUAJE. This tool aims to design, analyze and evaluate fuzzy systems with good interpretability-accuracy trade-off. In addition, GUAJE includes several intuitive and interactive tutorials to uncover the possibilities it offers. One of them generates and enhances a fuzzy system, analyzing each improvement through the use of fingrams, and lets the user reproduce the illustrative case study described in this paper.