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

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