Stacking classifiers for anti-spam filtering of e-mail

Simple item page
dc.creatorSakkis, G.
dc.creatorAndroutsopoulos, I.
dc.creatorPaliouras, G.
dc.creatorKarkaletsis, V.
dc.creatorSpyropoulos, C. D.
dc.creatorStamatopoulos, P.
dc.date2001-06-19
dc.date.accessioned2026-07-07T03:17:16Z
dc.date.available2026-07-07T03:17:16Z
dc.descriptionWe 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.
dc.identifierhttps://arxiv.org/abs/cs/0106040
dc.identifierhttp://arxiv.org/abs/cs/0106040
dc.identifierProceedings of "Empirical Methods in Natural Language Processing" (EMNLP 2001), L. Lee and D. Harman (Eds.), pp. 44-50, Carnegie Mellon University, Pittsburgh, PA, 2001
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/30658
dc.subjectComputation and Language
dc.subjectArtificial Intelligence
dc.subjectH.4.3; I.2.6; I.2.7; I.5.4; K.4.1
dc.titleStacking classifiers for anti-spam filtering of e-mail
dc.typetext

Files

Collections

arXiv