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Type: Artigo de periódico
Title: Facing the spammers: A very effective approach to avoid junk e-mails
Author: Almeida, TA
Yamakami, A
Abstract: Spam has become an increasingly important problem with a big economic impact in society. Spam filtering poses a special problem in text categorization, in which the defining characteristic is that filters face an active adversary, which constantly attempts to evade filtering. In this paper, we present a novel approach to spam filtering based on the minimum description length principle and confidence factors. The proposed model is fast to construct and incrementally updateable. Furthermore, we have conducted an empirical experiment using three well-known, large and public e-mail databases. The results indicate that the proposed classifier outperforms the state-of-the-art spam filters. (C) 2011 Elsevier Ltd. All rights reserved.
Subject: Minimum description length
Confidence factors
Spam filter
Text categorization
Machine learning
Country: Inglaterra
Editor: Pergamon-elsevier Science Ltd
Citation: Expert Systems With Applications. Pergamon-elsevier Science Ltd, v. 39, n. 7, n. 6557, n. 6561, 2012.
Rights: fechado
Identifier DOI: 10.1016/j.eswa.2011.12.049
Date Issue: 2012
Appears in Collections:Unicamp - Artigos e Outros Documentos

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