Boosting Trees for Anti-Spam Email Filtering
| dc.creator | Carreras, Xavier | |
| dc.creator | Marquez, Lluis | |
| dc.date | 2001-09-13 | |
| dc.date.accessioned | 2026-07-07T03:17:29Z | |
| dc.date.available | 2026-07-07T03:17:29Z | |
| dc.description | 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. | |
| dc.description | 7 pages, 13 figures | |
| dc.identifier | https://arxiv.org/abs/cs/0109015 | |
| dc.identifier | http://arxiv.org/abs/cs/0109015 | |
| dc.identifier | Proceedings of RANLP-2001, pp. 58-64, Bulgaria, 2001 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/30740 | |
| dc.subject | Computation and Language | |
| dc.subject | I.2.7;I.5.4 | |
| dc.title | Boosting Trees for Anti-Spam Email Filtering | |
| dc.type | text |