Analysing the Hierarchical Fuzzy Rule Based Classification Systems with genetic rule selection

This contribution is focused on the enhancement of the precision for Fuzzy Rule Based Classification Systems by the refinement of the Knowledge Base. Specifically, we make use of a Hierarchical Fuzzy Rule Based Classification System, which consists in the application of a thicker granularity in order to generate the initial Rule Base, and to reinforce those problem subspaces that are specially difficult by means of the application of rules with a higher granularity. Furthermore, we will perform a genetic rule selection process in order to obtain a compact and accurate model. Our experimental results show the goodness of this approach, especially when the number of classes is high, which usually implies a higher difficulty in the separability of the examples. Our conclusions are supported by means of the corresponding statistical tests.