Dynamic classifier selection using clustering for spam detection
Mehrnoush Famil Saeedian, Hamid Beigy · 2009
Most email users have encountered with spam problems, which have been addressed as a text classification or categorization problem. In this paper, we propose a novel spam detection method that uses ensemble of classifiers based on clustering and selection techniques. There is diversity in genre of e-mail's content and this method can find different topics in emails by clustering. It first computes disjoint clusters of emails, and then a classifier is trained on each cluster. When new email arrives, its cluster is identified. The classifier of the identified cluster is selected to classify the new email. Our method can extract many kinds of topics in emails. The evaluation shows that the algorithm outperforms majority voting.