Improving the OVO performance in Fuzzy Rule-Based Classification Systems by the genetic learning of the granularity level

This contribution proposes a genetic learning process for designing the knowledge base of Fuzzy Rule-Based classification Systems, that will be used as binary classifiers in a One-vs-One decomposition for multi-class problems. A Genetic Algorithm is designed to adapt the number of fuzzy labels per variable (granularity level) for each classifier in order to improve the accuracy rate of a multi-class classifier. The genetic learning process evolves granularity levels and needs a fuzzy rules generation method for generating the whole knowledge base of the Fuzzy System. Several data-sets from KEEL data-set repository are used in the experimental study and we compare our proposal with three related methods: the standard way to design Fuzzy Rule-Based Classification Systems using the fuzzy rules generation method chosen with and without One-vs-One decomposition, and our proposal of genetic granularity level learning without One-vs-One decomposition.