Training SpamAssassin with Active Semi-supervised Learning

Most spam filters include some automatic pattern classifiers based on machine learning and pattern recognition techniques. Such classifiers often require a large training set of labeled emails to attain a good discriminant capability between spam and legitimate emails. In addition, they must be frequently updated because of the changes introduced by spammers to their emails to evade spam filters. To address this issue active learning and semi-supervised learning techniques can be used. Many spam filters allow the user to give a feedback on personal emails automatically labeled during filter operation, and some filters include a self-training mechanism to exploit the large number of unlabeled emails collected during filter operation. However, users are usually willing to label only a few emails, and the benefits of selftraining techniques are limited. In this paper we propose an active semi-supervised learning method to better exploit unlabeled emails, which can be easily implemented as a plug-in in real spam filters. Our method is based on clustering unlabeled emails, querying the label of one email per cluster, and propagating such label to the most similar emails of the same cluster. The effectiveness of our method is evaluated using the well known open source SpamAssassin filter, on a large and publicly available corpus of real legitimate and spam emails.