Structural learning of fuzzy rules from noised examples
Antonio González, Raúl Pérez · 2002
Inductive learning algorithms obtain the knowledge of a system from a set of examples. One of the most difficult problems in machine learning is to obtain the structure of this knowledge. We propose an algorithm which is able to manage fuzzy information and to learn the structure of the rules that represent the system. The algorithm gives a reasonable small set of fuzzy rules that represent the original set of examples.>