Using the OLS algorithm to build interpretable rule bases: an application to a depollution problem

One of the main advantages of fuzzy modeling is the ability to yield interpretable results. Amongst these modeling methods, the OLS algorithm is a mathematically robust technique that allows to induce a fuzzy rule base from a set of training data. It does so by using linear regression to select the most important rules. However, the original OLS algorithm only relies upon numerical accuracy, and doesn´t take interpretability matters into account. Thus, we propose some modifications to the original method so that it builds interpretable rule bases.