An iterative strategy for feature construction on a fuzzy rule-based learning algorithm

This paper presents a proposal for using feature construction in a fuzzy rule-based learning algorithm as a method to avoid working with a fixed set of features to describe a particular problem. The main purpose is to increase the amount of information extracted from initial variables to construct a model that has better prediction capability. This approach iteratively looks for the function that obtains the best adaptation level to the examples covered by a rule. If exists, this function is added both to the antecedent of the rule and to a specific structure called catalog of functions in order to be considered by the learning algorithm. For that, a model of rule is used in order to represent this kind of knowledge in combination with the catalog, which helps us to manage the functions that have ever been considered during the learning process. Finally, a comparative study of the results obtained with this approach is presented.