Knowledge extraction using a genetic fuzzy rule-based system with increased interpretability

Abstract

In this paper a fuzzy rule-based system is trained to perform a classification task using a genetic algorithm and a fitness function that simultaneously considers the accuracy of the model and its interpretability. Initially a decision tree is created using any tree induction algorithm such as CART, ID3 or C4.5. This tree is then used to generate a fuzzy rule-based system. The parameters of the membership functions are adjusted by the genetic algorithm. As a case study, the proposed method is applied to an appendicitis dataset with 106 instances (input-output pairs), 7 normalized real-valued inputs and 1 binary output.