Using the OLS algorithm to build interpretable rule bases: an application to a depollution problem
Sébastien Destercke, Serge Guillaume, Brigitte Charnomordic · Proceedings of ... IEEE International Conference on Fuzzy Systems · 2007
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.