![]() SpamAssassin 2.63 (Standard configuration with no user feedback) withĪ threshold of 5.0. SpamAssassin 2.63 (Bayes component only) with a threshold of 0.0. SpamAssassin 2.63 (ad hoc component only) with the default threshold SpamAssassin 2.63 (both components) with the default threshold Prior training was not used filterinit initialized the filter’s memory to the empty We tested several configurations of SpamAssassin 2.63 so as to evaluate the relativeĬontributions of the ad hoc and Bayes components, and to evaluate various trainingĮxcept as noted, the filters were installed using default threshold and tuning Is classified as spam if the score exceeds the threshold. The overall score is compared to a fixed threshold the message Likelihood estimate is converted to a (possibly negative) weight which is added to the ad Previously-classified messages to estimate the likelihood that a particular message is spam. The Bayes filter, on the other hand, is adaptive – it uses statistics from Each ad hoc rule has a predetermined weight the weights ofįeatures observed in a particular message are summed to yield a combined spamminess Identify patterns associated with spam, and a Bayes filter fashioned from Graham’s and SpamAssassin contains two principal components: a set of static ad hoc rules that When the email for the test corpus was collected. With one another, and with the in situ performance of SpamAssassin, which was deployed Roles and interactions of its various components. ![]() The other filters –īogofilter, CRM114, DSPAM, SpamBayes, and SpamProbe – areĪll ‘pure’ statistical learning systems, with only a few tacit rules such as those forįive different configurations of SpamAssassin were tested, in order to evaluate the Hand-coded spam-detection rules and a statistical learning component. Of the filters we selected, SpamAssassin is a hybrid system which includes Or ‘Bayesian’ filters owing their heritage to Graham’s A Plan for Spam with We adopt Fisher’s view ] of an infinite hypothetical population.Ģ Idealized in that feedback to the filter is immediate and consistently accurate. ![]() Rule bases, internal or external black lists and white lists, and content-based ‘statistical’ġ The notion of population has been the subject of historical and current philosophical debate. Reported in the literature, an extensive search of available practical email filters yieldedįilters that used only a limited number of techniques, which we characterize as hand-coded Large number of classification techniques potentially relevant to spam filtering had been Widely reported on the internet and in the popular press. We selected the current versions of six open-source filters whose deployment had been We used the method suggested by each filter’s documentation. (train on everything) while others require thatfiltertrain be invoked only for misclassified Some filters require thatfiltertrain be invoked for every message ![]() filterinit sets theįilter’s memory to a clean initial state filtereval is given an email message and returns aĬlassification and a spamminess score filtertrain is given an email message and the gold Three common interface procedures: filterinit, filtereval,andfiltertrain. Headers, in the same order as originally delivered. ![]() The subject filter is presented with each message, with original Which the email collection was originally delivered, with a different filter in place of Each trial run is an idealized 2 reproduction of X’s behaviour over the eight months in ![]()
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