E-Mail Spam Filtering by a New Hybrid Feature Selection Method Using IG as Filter and Random Tree as Wrapper

In this paper, we want to present a new method in e-mail spam filtering to improve the accuracy. We are interested in this topic, because receiving a high volume of unwanted messages (called spams) which are increasing day by day, have become commonplace for users. This method uses a new hybrid architecture on feature selection phase, by using the combination of two filtering models, Filter and Wrapper, with IG (Information Gain) filter and Random Tree wrapper as feature selectors. In addition, Multinomial Naïve Bayes (MNB) classifier, Support Vector Machine (SVM) classifier and Random Forest classifier are used for classification. Finally, the output results are examined and the best design is selected and it is compared with another similar works by considering different parameters. The optimal accuracy of the proposed system is evaluated equal to 98%.