Improving naive Bayes classifiers using neuro-fuzzy learning

Abstract

Naive Bayes classifiers are a well-known and powerful type of classifier 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 classifier 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 mapping the latter to the former in order to improve its capabilities.