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Online supervised spam filter evaluation
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DOI：10.1145/1247715.1247717.png)
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Abstract
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Eleven variants of six widely used open-source spam filters are tested on a chronological sequence of 49086 e-mail messages received by an individual from August 2003 through March 2004. Our approach differs from those previously reported in that the test set is large, comprises uncensored raw messages, and is presented to each filter sequentially with incremental feedback. Misclassification rates and Receiver Operating Characteristic Curve measurements are reported, with statistical confidence intervals. Quantitative results indicate that content-based filters can eliminate 98% of spam while incurring 0.1% legitimate email loss. Qualitative results indicate that the risk of loss depends on the nature of the message, and that messages likely to be lost may be those that are less critical. More generally, our methodology has been encapsulated in a free software toolkit, which may used to conduct similar experiments.
Keywords:
experimentation
measurement
spam
email
text classification
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