Using a sequential covering strategy for discovering fuzzy rules incrementally

The sequential covering strategy has been and still is a very common way to develop rule learning algorithms. This strategy follows a greedy procedure to learn rules, where, after each step one rule is obtained. Recently, we proposed a new sequential covering strategy that allowed the review of previously learned knowledge during the learning process itself. This review of knowledge allowed the algorithm to adapt to changes that may occur in the context of learning. Specifically, in this paper we consider the changes produced by the addition of new training examples, and therefore we make a proposal of incremental learning of fuzzy rules. We have performed several experiments to test the behavior of the proposal and the results have been very promising.