Boosting Trees for Anti-Spam Email Filtering
<|reference_start|>Boosting Trees for Anti-Spam Email Filtering: 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.<|reference_end|>
@article{carreras2001boosting,
    title={Boosting Trees for Anti-Spam Email Filtering},
    author={Xavier Carreras and Lluis Marquez},
    journal={Proceedings of RANLP-2001, pp. 58-64, Bulgaria, 2001},
    year={2001},
    archivePrefix={arXiv},
    eprint={cs/0109015},
    primaryClass={cs.CL}
}