A Personalized Spam Filtering Approach Utilizing Two Separately Trained Filters

摘要

By feeding personal e-mails into the training set, personalized content-based spam filters are believed to classify e-mails in higher accuracy. However, filters trained by both spam mails and personal mails may have difficulty classifying e-mails with the same characteristics of both spam and ham. In this paper, we propose a two-tier approach of using two filters trained only with either personal mails or spam mails. E-mails classified as legitimate mails by the legitimate mail filter may pass, while the remaining e-mails are processed by the spam filter in an ordinary way. Experiments in this paper are performed on two mail servers–one equipped with ordinary spam filter, and the other equipped both the legitimate mail filter and the spam filter. By combining the two filters with tuned thresholds, a much lower false positive rate is observed under the same false negative rate comparing to the ordinary filter.