Automatic Email Spam Detection using Genetic Programming with SMOTE
Maria Habib, Hossam Faris, Mohammad A. Hassonah, Ja’far Alqatawna, Alaa F. Sheta, Ala’ M. Al-Zoubi · 2018
Being one of the major communication ways on the Internet, the emailing systems need to be protected from spam which represents unsolicited messages with serious threats to both individual users and organizations. Realizing this issue, it is an imperious necessity to develop more accurate and effective spam detection models for the emailing platforms. In this paper, an efficient email spam detection model based on Genetic Programming (GP) combined with Synthetic Minority Over-sampling Technique (SMOTE) is proposed to detect spam emails. The model is applied and tested on two benchmark email corpora and tested against four other well-recognized classifiers using four measures; accuracy, recall, precision and G-mean. Experimental results show that GP combined with SMOTE can effectively classify spam emails outperforming the usual classification methods.