An E-mail filtering approach using classification techniques

Abstract

E-mail is one of the most popular ways of communication due to its accessibility, low sending cost and fast message transfer. However, Spam emails appear as a severe problem affecting this application of today’s Internet. Filtering is an important approach to isolate those spam emails. In this paper, an approach for filtering spam email is proposed, which is based on classification techniques. The approach analyses the body of Email messages and assigns weights to terms (features) that can help identifying spam and clean (ham) emails. An adaptation is proposed that tries to reduce the dimensionality of the extracted features, in which only determined (meaningful) terms are regarded by consulting a dictionary. A thorough comparative study has been studied among different classification algorithms that prove the efficiency of the filtering approach proposed. The approach has been evaluated using Enron dataset.