A feature construction approach for genetic iterative rule learning algorithm
A method to include feature construction in a fuzzy rule learning algorithm is proposed. The feature construction incorporates relations and functions in the antecedent of fuzzy rules. This procedure allows to increase the amount of information extracted from the initial variables. The proposal NSLV-FR obtains results with a good balance among accuracy, interpretability and time needed to get the model.
feature construction genetic fuzzy systems iterative learning approach classification