Email filtering based on supervised learning and mutual information feature selection

Electronic mail is one of today´s most important ways to communicate and transfer information. Because of fast delivery and easy to access, it is used almost in every aspect of communication in work and life. However, the increase in email users has resulted in a dramatic increase in spam emails during the past few years. In this paper, we propose an email-filtering approach that is based on supervised classifier and mutual information. The proposed model has the advantage of combining machine supervised learning with feature selection. Term frequency (TF) is presented to assign relevance weights to words of each email class. We conduct experiments to compare between six different classifiers. Results show that the proposed approach has high performance in terms of precision, recall and accuracy performance measures.