Three interface functions used in algorithm 1 - filterinit, filtereval, and filtertrain Testing and deployment in the supervised configuration (figure 4). Spam filter designers should incorporate interfaces making them amenable for Predicted by the author, the result was substantially worse. Instructions, we ran CRM-114 in a train-on-everything configuration and, as In an effort to ensure that we had not misinterpreted the installation This configuration could account for a slow learning rateĪs each system avails itself of the information in only about 1,000 of the 50,000 The observed rate to achieve the same misclassification rates as the other systems.īoth these systems were designed to be used in a train on error configuration,Īnd do not self-train. Rate would be too slow for personal email filtering as it would take several years at From a practical standpoint, this learning The email stream, leading us to conjecture that their performance might asymptoticallyĪpproach that of the other filters. Both exhibit substantial learning throughout Setting, if any, are small and observable only when the ham misclassificationĬRM-114 and DSPAM exhibit substantially inferior performance to the other filters, ROC analysis shows that the differences not accountable to threshold Each shows a different tradeoff between ham accuracy and The choice of threshold parameters dominates the observed differences in performanceĪmong the four filters implementing methods derived from Graham's and Rules alone, but not as well as the combination of the two. The supervised filter alone performed better than than the static Two unsupervised configurations also improved the static component, but byĪ smaller margin.
Static component, as measured by both ham and spam misclassification probabilities. Messages, which comprise the largest and most critical fraction of ham.Ī supervised filter contributes significantly to the effectiveness of Spamassassin's InĬontrast, the best filters misclassified no personal messages, and no delivery error Likely to notice, should the message go astray, and retrieve it from the spam file. (or soon after starting to use the filter), a time at which the user would be more
We might also conjecture that these misclassificationsĪre more likely to occur soon after subscribing to the particular service Of each advertiser, news service, mailing list, or on-line service from which the Number of misclassifications suggests that the filter rapidly learns the characteristics That the filters find them more difficult to classify. That such messages represent a small fraction of incoming mail, we may conclude Or the results of electronic transactions. Most misclassified ham messages are advertising, news digests, mailing list messages, Messages early in the test suite none within the second half (25,000 messages).Ī larger study will be necessary to distinguish the asymptotic probability of ham The best-performing filters misclassified a handful of spam The corresponding risk of mail loss, while minimal, isĭifficult to quantify. The best-performingįilters reduced the volume of incoming spam from about 150 messages per day toĪbout 2 messages per day. Supervised spam filters are effective tools for attenuating spam. And a long document it is (funny placeholder images though.) Here's the conclusions for the impatient but interested in a little more than the summary:
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