Abstract

This paper describes a set of comparative experiments for the problem of automatically ltering unwanted electronic mail messages. Several variants of the AdaBoost algorithm with con dence{ rated predictions (Schapire & Singer 99) have been applied, which di er in the complexity of the base learners considered. Two main conclusions can be drawn from our experiments: a) The boosting{based methods clearly outperform the baseline learning algorithms (Naive Bayes and Induction of Decision Trees) on the PU1 corpus, achieving very high levels of the F1 measure � b) Increasing the complexity of the base learners allows to obtain better \high{precision " classi ers, which isavery important issue when misclassication costs are considered. 1

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