In this work, a set of comparative experiments for the problem of automatically filtering unwanted electronic mail messages are performed on two public corpora: PU1 and LingSpam. Several variants of the AdaBoost algorithm with confidence-rated predictions (Schapire et al., 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 other learning algorithms results published on the two evaluation corpora, achieving very high levels of the F_1 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.