Stacking classifiers for anti-spam filtering of e-mail
<|reference_start|>Stacking classifiers for anti-spam filtering of e-mail: We evaluate empirically a scheme for combining classifiers, known as stacked generalization, in the context of anti-spam filtering, a novel cost-sensitive application of text categorization. Unsolicited commercial e-mail, or "spam", floods mailboxes, causing frustration, wasting bandwidth, and exposing minors to unsuitable content. Using a public corpus, we show that stacking can improve the efficiency of automatically induced anti-spam filters, and that such filters can be used in real-life applications.<|reference_end|>
@article{sakkis2001stacking,
    title={Stacking classifiers for anti-spam filtering of e-mail},
    author={G. Sakkis, I. Androutsopoulos, G. Paliouras, V. Karkaletsis, C. D.
  Spyropoulos and P. Stamatopoulos},
    journal={Proceedings of "Empirical Methods in Natural Language Processing"
  (EMNLP 2001), L. Lee and D. Harman (Eds.), pp. 44-50, Carnegie Mellon
  University, Pittsburgh, PA, 2001},
    year={2001},
    archivePrefix={arXiv},
    eprint={cs/0106040},
    primaryClass={cs.CL cs.AI}
}