Abstract
The growing problem of unsolicited bulk e-mail, also known as "spam", has generated a need for reliable anti-spam e-mail filters. Filters of this type have so far been based mostly on manually constructed keyword patterns. An alternative approach has recently been proposed, whereby a Naive Bayesian classifier is trained automatically to detect spam messages. We test this approach on a large collection of personal e-mail messages, which we make publicly available in "encrypted" form contributing towards standard benchmarks. We introduce appropriate cost-sensitive measures, investigating at the same time the effect of attribute-set size, training-corpus size, lemmatization, and stop lists, issues that have not been explored in previous experiments. Finally, the Naive Bayesian filter is compared, in terms of performance, to a filter that uses keyword patterns, and which is part of a widely used e-mail reader.
Keywords
Computer scienceNaive Bayes classifierClassifier (UML)Set (abstract data type)Bayesian probabilityInformation retrievalFilter (signal processing)Machine learningArtificial intelligenceData miningPersonally identifiable informationSupport vector machineComputer security
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Publication Info
- Year
- 2000
- Type
- article
- Pages
- 160-167
- Citations
- 446
- Access
- Closed
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Cite This
Ion Androutsopoulos,
John Koutsias,
Konstantinos V. Chandrinos
et al.
(2000).
An experimental comparison of naive Bayesian and keyword-based anti-spam filtering with personal e-mail messages.
, 160-167.
https://doi.org/10.1145/345508.345569
Identifiers
- DOI
- 10.1145/345508.345569