Stacking classifiers for anti-spam filtering of e-mail

Details Stacking classifiers for anti-spam filtering of e-mail Stacking classifiers for anti-spam filtering of e-mail

2001-06-19

arxiv.org/abs/cs/0106040v1

Proceedings of "Empirical Methods in Natural Language Processing" (EMNLP 2001), L. Lee and D. Harman (Eds.), pp. 44-50, Carneg

Computer Science

Computation and Language

Scientific paper

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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