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|Type:||Artigo de periódico|
|Title:||Occam's Razor-based Spam Filter|
|Abstract:||Nowadays e-mail spam is not a novelty, but it is still an important rising problem with a big economic impact in society. Spammers manage to circumvent current spam filters and harm the communication system by consuming several resources, damaging the reliability of e-mail as a communication instrument and tricking recipients to react to spam messages. Consequently, spam filtering poses a special problem in text categorization, of 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 theminimum description length principle. Furthermore, we have conducted an empirical experiment on six public and real non-encoded datasets. The results indicate that the proposed filter is fast to construct, incrementally updateable and clearly outperforms the state-of-the-art spam filters. © The Brazilian Computer Society 2012.|
|Appears in Collections:||Unicamp - Artigos e Outros Documentos|
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