arXiv cs/01090152001-09-13cs.CL

Boosting Trees for Anti-Spam Email Filtering

Xavier Carreras, Lluis Marquez

备注：: 7 pages, 13 figures
发表信息：: Proceedings of RANLP-2001, pp. 58-64, Bulgaria, 2001

英文摘要：

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.

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