COR Methodology: A Simple Way to Obtain Linguistic Fuzzy Models with Good Interpretability and Accuracy

Abstract

Abstract. The chapter introduces a simple learning methodology, the cooperative rules (COR) one, that improves the accuracy of linguistic fuzzy models preserving the highest interpretability. Its operation mode involves a combinatorial search of fuzzy rules performed over a set of previously generated candidate ones.The accuracy is achieved by developing a smart search space reduction and by inducing the generation of a linguistic fuzzy rule set with good cooperation. COR also ensures a good interpretability by keeping the membership functions and the model structure unaltered, as well as generating a compact rule base.

Fuzzy Association Rules for Estimating Consumer Behaviour Models and Their Application to Explaining Trust in Internet Shopping

“…We use a specific ant colony optimization algorithm as the optimization technique. For a deeper description of the COR methodology and the use of this optimization technique, refer to Casillas et al (2003c).…”

Learning weighted linguistic rules to control an autonomous robot

“…20 and extended in Ref. 21). In this way, considering a miso (multiple input single output) fuzzy system, for each possible antecedent combination in the problem input space, this method automatically learns the best consequent label and its associated weight.…”