Improving Naive Bayes Classiiers Using Neuro-fuzzy Learning 1

Naive Bayes classi ers are a well-known and powerful type of classi ers that can easily be induced from a dataset of sample cases. However, the strong conditional independence and distribution assumptions underlying them can sometimes lead to poor classi cation performance. Another prominent type of classi ers are neuro-fuzzy classi cation systems, which derive (fuzzy) classi ers from data using neural-network inspired learning methods. Since there are certain structural similarities between a neuro-fuzzy classi er and a naive Bayes classi er, the idea suggests itself to map the latter to the former in order to improve its capabilities.