An Experimental Comparison of Naive Bayesian and Keyword-Based Anti-Spam Filtering with Personal E-mail Messages (2000)
| Venue: | In Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval |
| Citations: | 74 - 2 self |
BibTeX
@INPROCEEDINGS{Androutsopoulos00anexperimental,
author = {Ion Androutsopoulos and John Koutsias and Konstantinos V. Cb and Constantine D. Spyropoulos},
title = {An Experimental Comparison of Naive Bayesian and Keyword-Based Anti-Spam Filtering with Personal E-mail Messages},
booktitle = {In Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval},
year = {2000},
pages = {160--167},
publisher = {ACM Press}
}
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Abstract
The growing problem of unsolicited bulk e-mail, also known as "spare", 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 filtering/routing; text categorization; machine learning and IR; evaluation (general); test collections I.
