Improving Naive Bayes Classifiers Using Neuro-Fuzzy Learning

Naive Bayes classifiers are a well-known and powerful type of classifiers 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 classification performance. Another prominent type of classifiers are neuro-fuzzy classification systems, which derive (fuzzy) classifiers from data using neural-network inspired learning methods. Since there are certain structural similarities between a neuro-fuzzy classifier and a naive Bayes classifier, the idea suggests itself to map the latter to the former in order to improve its capabilities.