In this paper, we report our work on spam filtering with three novel Bayesian classification
methods: aggregating one-dependence estimators (AODE), hidden Naive Bayes (HNB) …

Earlier works on detecting spam e-mails usually compare the contents of e-mails against
specific keywords, which are not robust as the spammers frequently change the terms used …

Technological advances are accelerating the dissemination of information. Today, millions
of devices and their users are connected to the Internet, allowing businesses to interact with …

Using AdaBoost and Decision Stumps to Identify Spam E-mail Page 1 Using AdaBoost and
Decision Stumps to Identify Spam E-mail Tyrone Nicholas June 4, 2003 Abstract An existing …

Most e-mail readers spend a non-trivial amount of time regularly deleting junk e-mail (spam)
messages, even as an expanding volume of such e-mail occupies server storage space and …

The rapid growth of unsolicited and unwanted messages has inspired the development of
many anti-spam methods. Machine-learning methods such as Naïve Bayes (NB), support …

Spam or unsolicited e-mail has become a major problem for companies and private users.
This thesis explores the problems associated with spam and some different approaches …

Junk e-mail detection and filtering can be considered a cost-sensitive classification problem.
Nevertheless, preprocessing methods and noise reduction strategies used to enhance the …

This paper describes a set of comparative experiments for the problem of automatically
filtering unwanted electronic mail messages. Several variants of the AdaBoost algorithm with …

It has recently been argued that a Naive Bayesian classifier can be used to filter unsolicited
bulk e-mail (" spam"). We conduct a thorough evaluation of this proposal on a corpus that we …