A Bayesian Approach to Filtering Junk E-Mail

Published 1 July 1998

Mehran Sahami, Susan Dumais, David Heckerman, Eric Horvitz

Citations1,153

TL;DR

This work examines methods for the automated construction of filters to eliminate such unwanted messages from a user’s mail stream, and shows the efficacy of such filters in a real world usage scenario, arguing that this technology is mature enough for deployment.

Abstract

In addressing the growing problem of junk E-mail on the Internet, we examine methods for the automated construction of filters to eliminate such unwanted messages from a user&apos;s mail stream. By casting this problem in a decision theoretic framework, we are able to make use of probabilistic learning methods in conjunction with a notion of differential misclassification cost to produce filters which are especially appropriate for the nuances of this task. While this may appear, at first, to be a straight-forward text classification problem, we show that by considering domain-specific features of this problem in addition to the raw text of E-mail messages, we can produce much more accurate filters. Finally, we show the efficacy of such filters in a real world usage scenario, arguing that this technology is mature enough for deployment. Introduction As the number of users connected to the Internet continues to skyrocket, electronic mail (E-mail) is quickly becoming one of the fastest and m...

Keywords

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