Paper: Stacking Classifiers For Anti-Spam Filtering Of E-Mail

ACL ID W01-0506
Title Stacking Classifiers For Anti-Spam Filtering Of E-Mail
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2001
Authors

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.