Because of the changing nature of spam, a spam filtering system that uses machine learning will need to be dynamic. This suggests that a case-based (memory-based) approach may work...
Sarah Jane Delany, Padraig Cunningham, Lorcan Coyl...
Abstract. In adversarial classification tasks like spam filtering, intrusion detection in computer networks and biometric authentication, a pattern recognition system must not only...
Recent email spam filtering evaluations, such as those conducted at TREC, have shown that near-perfect filtering results are attained with a variety of machine learning methods wh...
As a side effect of e-marketing strategy the number of spam e-mails is rocketing, the time and cost needed to deal with spam as well. Spam filtering is one of the most difficult t...
Motivated by current efforts to construct more realistic spam filtering experimental corpora, we present a newly assembled, publicly available corpus of genuine and unsolicited (s...
The paper presents a brief survey of the fight between spammers and antispam software developers, and also describes new approaches to spam filtering. In the first two sections we...
The email communication system is threatened by unsolicited commercial email aka spam. In response, spam filters have been deployed widely to help reduce the amount of spam users ...
Active learning methods seek to reduce the number of labeled examples needed to train an effective classifier, and have natural appeal in spam filtering applications where trustwo...
The volume of spam e-mails has grown rapidly in the last two years resulting in increasing costs to users, network operators, and e-mail service providers (ESPs). E-mail users dem...
The purpose of this research is to propose an appropriate classification approach to improving the effectiveness of spam filtering on the issue of skewed class distributions. A cl...