A graph based semi-supervised method for email spam filtering, based on the local and global consistency method, yields low error rates with very few labeled examples. The motivating application of this method is spam filters with access to very few labeled message. For example, during the initial deployment of a spam filter, only a handful of labeled examples are available but unlabeled examples are plentiful. We demonstrate the performance of our approach on TREC 2007 and CEAS 2008 email corpora. Our results compare favorably with the best-known methods, using as few as just two labeled examples: one spam and one non-spam.

The results of the 2006 ECML/PKDD Discovery Challenge suggest that semi-supervised learning methods work well for spam filtering when the source of available labeled examples differs from those to be classified. We have attempted to reproduce these results using data from the 2005 and 2007 TREC Spam Track, and have found the opposite effect: methods like self-training and transductive support vector machines yield inferior classifiers to those constructed using supervised learning on the labeled data alone. We investigate differences between the ECML/PKDD and TREC data sets and methodologies that may account for the opposite results.