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Spam filtering using Bayesian networks Public

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The proliferation of spam e-mail is a rising problem. Not only an annoyance, spam wastes valuable network space and routing time. Due to high spam volume, manual filtering is inadequate. Automated spam filters exist, but better ones could help mitigate its impact. The effectiveness of Bayesian Networks for spam filtering was compared to the Naive Bayes approach, common to commercial filters. Bayesian Networks were found to produce higher quality predictions at acceptable speeds.

  • This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.
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  • 05D035M
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  • 2005
Date created
  • 2005-01-01
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