Advances in Spam Filtering Techniques

Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 394)

Abstract

Nowadays e-mail spam is not a novelty, but it is still an important rising problem with a big economic impact in society. Fortunately, there are different approaches able to automatically detect and remove most of those messages, and the best-known ones are based on machine learning techniques, such as Naïve Bayes classifiers and Support Vector Machines. However, there are several different models of Naïve Bayes filters, something the spam literature does not always acknowledge. In this chapter, we present and compare seven different versions of Naïve Bayes classifiers, the well-known linear Support Vector Machine and a new method based on the Minimum 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 easy to implement, incrementally updateable and clearly outperforms the state-of-the-art spam filters.

Keywords

Support Vector Machine Collaborative Filter Minimum Description Length Linear Support Vector Machine Spam Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.School of Electrical and Computer EngineeringUniversity of Campinas – UNICAMPCampinasBrazil

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