Building an interpretable fuzzy rule base from data using Orthogonal Least Squares Application to a depollution problem

TL;DR

This paper enhances the Orthogonal Least Squares algorithm to create interpretable fuzzy rule bases from data, demonstrating its effectiveness on benchmark and real-world depollution fault detection problems.

Contribution

It introduces modifications to the OLS algorithm to improve interpretability of fuzzy rule bases, applicable to practical depollution fault detection tasks.

Findings

Modified OLS improves rule interpretability

Effective on benchmark problems

Successful application to depollution fault detection

Abstract

In many fields where human understanding plays a crucial role, such as bioprocesses, the capacity of extracting knowledge from data is of critical importance. Within this framework, fuzzy learning methods, if properly used, can greatly help human experts. Amongst these methods, the aim of orthogonal transformations, which have been proven to be mathematically robust, is to build rules from a set of training data and to select the most important ones by linear regression or rank revealing techniques. The OLS algorithm is a good representative of those methods. However, it was originally designed so that it only cared about numerical performance. Thus, we propose some modifications of the original method to take interpretability into account. After recalling the original algorithm, this paper presents the changes made to the original method, then discusses some results obtained from benchmark problems. Finally, the algorithm is applied to a real-world fault detection depollution problem.