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.
@article{arxiv.cs/0106040,
title = {Stacking classifiers for anti-spam filtering of e-mail},
author = {G. Sakkis and I. Androutsopoulos and G. Paliouras and V. Karkaletsis and C. D. Spyropoulos and P. Stamatopoulos},
journal= {arXiv preprint arXiv:cs/0106040},
year = {2007}
}