Stacking classifiers for anti-spam filtering of e-mail
G. Sakkis, I. Androutsopoulos, G. Paliouras, V. Karkaletsis, C. D., Spyropoulos, P. Stamatopoulos

TL;DR
This paper demonstrates that stacking classifiers enhances the effectiveness of anti-spam email filtering, showing practical improvements in real-world applications using a public dataset.
Contribution
It empirically evaluates the use of stacked generalization for cost-sensitive anti-spam filtering, a novel application in text categorization.
Findings
Stacking improves anti-spam filter efficiency
Filters can be effectively used in real-life scenarios
Empirical results on a public corpus support the approach
Abstract
We evaluate empirically a scheme for combining classifiers, known as stacked generalization, in the context of anti-spam filtering, a novel cost-sensitive application of text categorization. Unsolicited commercial e-mail, or "spam", floods mailboxes, causing frustration, wasting bandwidth, and exposing minors to unsuitable content. Using a public corpus, we show that stacking can improve the efficiency of automatically induced anti-spam filters, and that such filters can be used in real-life applications.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSpam and Phishing Detection · Text and Document Classification Technologies · Internet Traffic Analysis and Secure E-voting
