Learning to classify threatening e-mail
S. Balamurugan, Renugadevi Rajaram · International Journal of Artificial Intelligence and Soft Computing · 2008
In this paper we study supervised classification of e-mails. We consider the task of Threaten E-mail Detection (i.e., e-mail related to terrorism, fraud, etc.). In this supervised learning setting, we investigate the use of Data Mining classifiers for automatic threaten e-mail detection. We show that the Decision Tree (DT) is a good choice for this task as it runs fast on large and high dimensional databases, is easy to tune and is highly accurate, outperforming popular algorithms such as Support Vector Machines (SVM), Naive Bayes (NB). In particular we are interested in detecting fraudulent, and possibly criminal, activities from such e-mail.