A comparison of ensemble and case-base maintenance techniques for handling concept drift in spam filtering

The problem of concept drift has recently received con-siderable attention in machine learning research. One important practical problem where concept drift needs to be addressed is spam filtering. The literature on con-cept drift shows that among the most promising ap-proaches are ensembles and a variety of techniques for ensemble construction has been proposed. In this pa-per we compare the ensemble approach to an alterna-tive lazy learning approach to concept drift whereby a single case-based classifier for spam filtering keeps it-self up-to-date through a case-base maintenance proto-col. The case-base maintenance approach offers a more straightforward strategy for handling concept drift than updating ensembles with new classifiers. We present an evaluation that shows that the case-base maintenance approach is as least as effective as/appears marginally more effective than a selection of ensemble techniques. The evaluation is complicated by the overriding impor-tance of False Positives (FPs) in spam filtering. The ensemble approaches can have very good performance on FPs because it is possible to bias an ensemble more strongly away from FPs than it is to bias the single clas-sifer. However this comes at considerable cost to the overall accuracy