Towards online learning of a fuzzy classifier

This study addresses issues related to the online applicability of a fuzzy classifier. In particular, it shows that a fuzzy classifier can be learned incrementally, and that in this process, imbalanced data sets, even when imbalance changes between classes can be used. Finally, it shows that for each class, examples and counter examples, can be effectively used. The most important aspect of the online fuzzy classifier is its perfect incremental aspect.