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btw, I was just rereading this -- an interesting approach you might
want to experiment with, is having *two* boundaries. ie:
negative scores positive scores
<----------------|-------------------|-------------------------->
| |
ham............ | .....unsure...... | ............spam
if a mail scores <= ham threshold, it's ham; >= spam threshold, it's spam;
and > ham threshold and < spam threshold, it's "unsure". this is similar
to the SpamBayes UI.
- --j.
Joe Flowers writes:
> I don't know if this will help anyone or not, but I wanted to report
> back just in case.
>
> In early April, I completely unhinged the dividing line between what SA
> score is used to mark a message as spam or ham (5.00 = default). This
> allows the system and this dividing line to drift "freely" to anywhere
> that SA will allow, without bound. This anti-spam setup has worked
> consistently much much better the whole time than in any previous
> implementation that we have done and with very little maintenance. We
> are very happy with it and are looking forward to implementing future SA
> versions in the same fashion.
>
> I'm not exactly sure the following numbers represent the whole time
> since April, but they should be pretty close.
>
> We've had 360,922 spam messages and 396,983 ham messages with a
> normalized average spam score of 6.8714134 and a normalized average ham
> score of -2.1532284. I have the divding line "set" at 30% of the
> distance between the average ham score and average spam score (30% above
> the average ham score). So, the dividing line is currently floating
> around 0.55416414.
>
> Apart from the default SA install, the only thing I have changed is
> 1. Turned off auto-learn <--- I think this is very important.
> 2. Set SA to ignore our custom spam score tag in the message headers.
>
> We are currently running SA v3.02.
>
> From time to time, but not very often (a couple of times every two
> weeks or so), I do feed bayes (sa-learn) with a few messages that are
> misplaced. I don't know the stats, but we have very few false positives,
> so I'm mostly feeding bayes with the false negatives which consist of
> the new/different message tricks that the spammers are using.
>
> Everyone here has been very happy with the results. It's been much much
> better than any implementation in the past.
> Many thanks to the SA developers! Rock on!
>
> Joe
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