Stacking classifiers for anti-spam filtering of e-mail

2026-07-072026-07-07http://salesiana.dossiersoluciones.com/handle/123456789/30658We 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.Computation and LanguageArtificial IntelligenceH.4.3; I.2.6; I.2.7; I.5.4; K.4.1Stacking classifiers for anti-spam filtering of e-mailtext