C4.5: programs for machine learning
C4.5: programs for machine learning
MailCat: an intelligent assistant for organizing e-mail
Proceedings of the third annual conference on Autonomous Agents
Foundations of statistical natural language processing
Foundations of statistical natural language processing
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Introduction to this special issue on revisiting and reinventing e-mail
Human-Computer Interaction
In search of coherence: a review of e-mail research
Human-Computer Interaction
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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.