A Spam Filter Evaluation
There is a recent study report on results of eleven variants of six widely used open-source spam filters including SpamBayes. The conclusion was "Supervised spam filters are effective tools for attenuating spam. The best-performing filters reduced the volume of incoming spam from about 150 messages per day to about 2 messages per day. The corresponding risk of mail loss, while minimal, is difficult to quantify." I'll leave it to those with more time on there hands than I to decipher the specific details on SpamBayes performance. The report is at http://plg.uwaterloo.ca/~gvcormac/spamcormack06.pdf
There is a recent study report on results of eleven variants of six widely used open-source spam filters including SpamBayes.
[...] I’ll leave it to those with more time on there hands than I to decipher the specific details on SpamBayes performance.
The report is at
Thanks for this. This study was one where filter developers themselves took part - Gordon (et al) didn't select the filters and do the evaluations independently (apart from a couple of extras). I looked after the SpamBayes submission (as well as managing one of the test corpora). If you want easier to read information about how SpamBayes did, my notebook paper or conference paper would be a good place (IMO) to start. Note that I didn't submit SpamBayes hoping to get comparatively good results (although it did surprisingly well); the unsure range, which is fundamental to SpamBayes, makes getting good results in a evaluation like this very difficult. The purpose was more to compare different methods of using SpamBayes (there were four variants evaluated). =Tony.Meyer -- Please always include the list (spambayes at python.org) in your replies (reply-all), and please don't send me personal mail about SpamBayes. http://www.massey.ac.nz/~tameyer/writing/reply_all.html explains this.
participants (2)
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Gil Hurlbut -
Tony Meyer