A methodology to generate compact and accurate fuzzy knowledge bases based on fuzzy clustering and evolutionary selection and tuning

A new methodology to learn descriptive linguistic Fuzzy Rule-based System Knowledge Bases from examples based on the combination of fuzzy clustering and evolutionary simultaneous rule selection and membership functions tuning is presented in this work. Fuzzy clustering is used to achieve a preliminary description of the data, in other words to obtain information on the definition of the linguistic terms and rules instead of predefined linguistic terms and rules that use them. The evolutionary algorithm obtains the final compact and accurate knowledge base selecting a subset of rules with high level of cooperation and fine-tuning the linguistic terms involved. The results obtained with this proposal improves accuracy as well as complexity through the number of rules compared with a classic algorithm and a reference algorithm both well known in the literature, as the experimental study developed shows, using several different data sets.