http://story.news.yahoo.com/news?tmpl=story&ncid=77&e=1&cid=77&u=/mc/20021114/tc_mc/spam_wars__spammer_says_it_is_almost_impossible_to_stop
Some of the spam tools claim a high success rate (>90%) and really low false positive rate (<1%).
To answere your question, I think you have to decide what your priorities are. If you are personally getting so much spam that it is overwhelming your ability to deal with it, then getting rid of all the spam may be more of a priority than getting a minimum of false positives.
When I worked on the KMail project, and used KMail as my MUA, I had a set of filters set up that would catch about 80% of the spam that came in on my system and filter it into a spam folder. I never had a false positive with those filters. I might still have that stuff on an old backup somewhere if someone is interested.
I toyed with the idea of using Markov chains (see JWZ's dadadodo) in an implementation of a spam filter. I was thinking of adapting the Markov chain algorithm as a machine learning technique. AFAIK, nobody has suggested using Markov chains for much other than fun.
I never got past the implementation of a simple Markov chain and a little proggie that would take "random" text from a mail file full of spam and rearrange it into the world's ultra-spam.--Hmm, that might make a fun addition to my web site. I'd better dig the code out of the archives, along with the file of spam.
[EMAIL PROTECTED] wrote:
On Wed, 13 Nov 2002, at 9:27am, [EMAIL PROTECTED] wrote:The biggest problem with a rule-based filter like SpamBouncer, of course, is the large number of false positives.
That is my big concern. The biggest thing keeping me from implementing serious anti-spam on my inbox at work is the risk of false positives. I get email from customers, often from weird addresses, and I cannot risk ignoring a potentially important email just because a customer can't spell. :)
Does anyone here have opinions on anti-spam solutions, when it is more important to minimize false positives than to catch all spam?
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