Title :
Learning to Classify Threaten E-mail
Author :
Balamurugan, S. Appavu alias ; Rajaram, R.
Author_Institution :
Dept. of Inf. Technol., Thiagarajar Coll. of Eng., Madurai
Abstract :
In this paper we study supervised classification of e-mails. We consider the task of threaten e-mail detection (i.e. email 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 decision tree 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, Naive Bayes. In particular, we are interested in detecting fraudulent and possibly criminal activities from such e-mails.
Keywords :
classification; data mining; decision trees; learning (artificial intelligence); unsolicited e-mail; automatic threaten e-mail detection; data mining classifier; decision tree; supervised e-mail classification; supervised learning; Asia; Data mining; Decision trees; Educational institutions; Electronic mail; Niobium; Supervised learning; Support vector machine classification; Support vector machines; Terrorism; Classification; DT; Data mining; NB.; SVM;
Conference_Titel :
Modeling & Simulation, 2008. AICMS 08. Second Asia International Conference on
Conference_Location :
Kuala Lumpur
Print_ISBN :
978-0-7695-3136-6
Electronic_ISBN :
978-0-7695-3136-6
DOI :
10.1109/AMS.2008.100
