I am interested in building a training suite for spamassassin, based
on past learning. The trouble is, I don't have my mail sorted exactly
into spam and ham. What I do have is an accurate bayes-seen database
created by spamassassin and corrected by scrupulous use of sa-learn.
I also have log of all my incoming mail (spam+ham).
So here's my question. Is there some easy way I can do this:
unfiltered-incoming-mail + bayes-seen ==> ham + spam + unclassified
Conceptually it is easy: read each mail, look it up in bayes-seen,
and send it to the appropriate file.
At one point I naively thought I might modify sa-learn to do this,
but it became apparent to me that it would take more than the time
I have available to understand sa-learn well enough to do so.
So my questions are these:
- is there an easy way to do this without modifying sa-learn?
- is there an easy way to add an option to sa-learn to do this?
(even if I had something like "sa-learn --query" that reported
spam/ham/unknown the remaining infrastructure would be easy
enough)
I think a solution to this problem would be of general interest.
It is often not convenient to maintain separate vetted ham/spam files.
For example, my setup is as follows. I filter my mail on a central
machine and then distribute the ham to various mail clients (laptop,
home & work desktops ...). The allgeged spam is saved on the central
server. On the clients I report misclassified stuff back to the
central machine, and in the alleged spam file I check for misclassified
stuff, so the bayes database is accurate. It is easy enough for the
central machine to keep every mail message, but I have no sensible
way for it to separate ham/spam, and to update the separate files
in response to error reports from the various clients.
If I had a solution to this problem I'd be in a position to contribute
to the release statistics, and to run other experiments.
Suggestions appreciated.
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