A case-based technique for tracking concept drift in spam filtering

Spam filtering is a particularly challenging machine learning task as the data distribution and concept being learned changes over time. It exhibits a particularly awkward form of concept drift as the change is driven by spammers wishing to circumvent spam filters. In this paper we show that lazy learning techniques are appropriate for such dynamically changing contexts. We present a case-based system for spam filtering that can learn dynamically. We evaluate its performance as the case-base is updated with new cases. We also explore the benefit of periodically redoing the feature selection process to bring new features into play. Our evaluation shows that these two levels of model update are effective in tracking concept drift.