Abstract
Despite the fact that different techniques have been developed to filter spam, due to the spammer’s rapid adoption of new spam detection techniques, we are still overwhelmed with spam emails. Currently, machine learning techniques are the most effective ways to classify and filter spam emails. In this paper, a comprehensive comparison and analysis of the performance of various classification models on the 2007 TREC Public Spam Corpus are exhibited in various cases of without or with N- Grams as well as using separate or combined datasets. It is shown that the inclusion of the N-Grams in the pre-processing phase provides high accuracy results for classification models in most of the cases, and the models using the split approach with combined datasets give better results than models using the separate dataset.
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