Stacking classifiers for anti-spam filtering of e-mail

G. Sakkis; I. Androutsopoulos; G. Paliouras; V. Karkaletsis; C. D. Spyropoulos; P. Stamatopoulos

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Stacking classifiers for anti-spam filtering of e-mail

G. Sakkis·I. Androutsopoulos·G. Paliouras·V. Karkaletsis·C. D. Spyropoulos·P. Stamatopoulos

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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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G. Sakkis, I. Androutsopoulos, G. Paliouras, V. Karkaletsis, C. D. Spyropoulos, P. Stamatopoulos. 2001-06-19. Stacking classifiers for anti-spam filtering of e-mail. https://arxiv.org/abs/cs/0106040

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