A two-step approach of feature construction for a genetic learning algorithm

Traditionally, fuzzy rule based models work with a fixed set of features to describe a particular problem. Our proposal is to use feature construction by means of functions in order to obtain new variables that allow us to get more information about the problem. In particular, we propose the use of previously defined functions over the input variables in the antecedent of the rules. This let us to know if a combination of variables is able to provide us with more information than each one of them separately. In addition, we use a structure that helps us to manage and also restrict the number of functions under consideration by the learning algorithm. We also present a new model of rule in order to represent this kind of knowledge by extending a basic learning fuzzy rule-based model. Finally, we show the experimental study associated with this work.