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Spam Filtering with Naive Bayes -- Which Naive Bayes? (2006)
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| Venue: | THIRD CONFERENCE ON EMAIL AND ANTI-SPAM (CEAS |
| Citations: | 48 - 3 self |
BibTeX
@INPROCEEDINGS{Metsis06spamfiltering,
author = {Vangelis Metsis and et al.},
title = {Spam Filtering with Naive Bayes -- Which Naive Bayes?},
booktitle = {THIRD CONFERENCE ON EMAIL AND ANTI-SPAM (CEAS},
year = {2006},
publisher = {}
}
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Abstract
Naive Bayes is very popular in commercial and open-source anti-spam e-mail filters. There are, however, several forms of Naive Bayes, something the anti-spam literature does not always acknowledge. We discuss five different versions of Naive Bayes, and compare them on six new, non-encoded datasets, that contain ham messages of particular Enron users and fresh spam messages. The new datasets, which we make publicly available, are more realistic than previous comparable benchmarks, because they maintain the temporal order of the messages in the two categories, and they emulate the varying proportion of spam and ham messages that users receive over time. We adopt an experimental procedure that emulates the incremental training of personalized spam filters, and we plot roc curves that allow us to compare the different versions of nb over the entire tradeoff between true positives and true negatives.
Keyphrases
naive bayes naive bayes naive bayes ham message di erent version fresh spam message entire tradeo particular enron user incremental training open-source anti-spam e-mail filter experimental procedure temporal order previous comparable benchmark several form anti-spam literature true positive new datasets personalized spam filter roc curve true negative non-encoded datasets
