SpamCop: A Spam Classi cation & Organization Program

Abstract

We present a simple, yet highly accurate, spam ltering program, called Spam-Cop, which is able to identify about 92% of the spams while misclassifying only about 1.16% of the nonspam e-mails. SpamCop treats an e-mail message as a multiset of words and employs a naive Bayes algorithm to determine whether or not a message is likely to be a spam. Compared with keyword-spotting rules, the probabilistic approach taken in SpamCop not only ooers high accuracy, but also overcomes the brittleness suuered by the keyword spotting approach.

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@inproceedings{Pantel1998SpamCopAS, title={SpamCop: A Spam Classi cation & Organization Program}, author={Patrick Pantel and Dekang Lin}, year={1998} }