  Using naive Bayes for email classification has become very popular within the
last few months. They are quite easy to implement and very efficient. In this
paper we want to present empirical results of email classification using a
combination of naive Bayes and k-nearest neighbor searches. Using this
technique we show that the accuracy of a Bayes filter can be improved slightly
for a high number of features and significantly for a small number of features.
