This item is available under a Creative Commons License for non-commercial use only
n this paper, we compare case-based spam filters, focusing on their resilience to concept drift. In particular, we evaluate how to track concept drift using a case-based spam filter that uses a feature-free distance measure based on text compression. In our experiments, we compare two ways to normalise such a distance measure, finding that the one proposed in  performs better. We show that a policy as simple as retaining misclassified examples has a hugely beneficial effect on handling concept drift in spam but, on its own, it results in the case base growing by over 30%. We then compare two different retention policies and two different forgetting policies (one a form of instance selection, the other a form of instance weighting) and find that they perform roughly as well as each other while keeping the case base size constant. Finally, we compare a feature-based textual case-based spam filter with our feature-free approach. In the face of concept drift, the feature-based approach requires the case base to be rebuilt periodically so that we can select a new feature set that better predicts the target concept. We find feature-free approaches to have lower error rates than their feature-based equivalents.
Delany, S. & Bridge, D. (2007) Catching the Drift: using Feature-Free Case-Based Reasoning for Spam Filtering. 7th. International Conference on Case-Based Reasoning (ICCBR), Belfast, Northern Ireland, 13-16 August.