Stacking classifiers for anti-spam filtering of e-mail
by Georgios Sakkis, Ion Androutsopoulos, George Paliouras, Vangelis Karkaletsis, Constantine D. Spyropoulos, Panagiotis Stamatopoulos
arXiv.org e-Print archive,
2001-06-19
Language:
English
Note: Proceedings of "Empirical Methods in Natural Language Processing" (EMNLP 2001), L. Lee and D. Harman (Eds.), pp. 44-50, Carnegie Mellon University, Pittsburgh, PA, 2001
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.
