Stacking classifiers for anti-spam filtering of e-mail

2001/06/19 by G. Sakkis, I. Androutsopoulos, G. Paliouras +3
Computer Science · #cs.CL #cs.AI

paper · pdf

published as 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

arxiv created 2001/06/19 · arxiv updated 2009/11/30

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.

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