Spam email filtering with bayesian belief network: using relevant words

Abstract

In this paper, we report our work on a Bayesian Belief Network approach to spam email filtering (classifying email as spam or nonspam/legitimate). Our evaluation suggests that a Bayesian Belief Network based classifier will outperform the popular Naive Bayes approach and two other famous learners: decision tree and k-NN. These four algorithms are tested on two different data sets with three different feature selection methods (Information Gain, Gain Ratio and Chi Squared) for finding relevant words. 10-fold cross-validation results show that Bayesian Belief Network performs best on both datasets. We suggest that this is because the 'dependant learner' characteristics of Bayesian Belief Network classification are more suited to spam filtering. The performance of the Bayesian Belief Network classifier could be further improved by careful feature subset selection.