New Learning Strategies for NEFCLASS

Neuro{fuzzy classiication systems ooer means to obtain fuzzy classiication rules by a learning algorithm. It is usually no problem to nd a suitable fuzzy classiier by learning from data, however, it can be hard to obtain a classiier that can be interpreted conveniently. In this paper we discuss extensions to the learning algorithms of NEFCLASS, a neuro{fuzzy approach for data analysis that we have presented before. We show how interactive strategies for pruning rules and variables from a trained classiier can enhance its interpretability.