  This paper describes a set of comparative experiments for the problem of
automatically filtering unwanted electronic mail messages. Several variants of
the AdaBoost algorithm with confidence-rated predictions [Schapire & Singer,
99] have been applied, which differ 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'' classifiers, which is a
very important issue when misclassification costs are considered.
