An evaluation of Naive Bayesian anti-spam filtering
<|reference_start|>An evaluation of Naive Bayesian anti-spam filtering: It has recently been argued that a Naive Bayesian classifier can be used to filter unsolicited bulk e-mail ("spam"). We conduct a thorough evaluation of this proposal on a corpus that we make publicly available, contributing towards standard benchmarks. At the same time we investigate the effect of attribute-set size, training-corpus size, lemmatization, and stop-lists on the filter's performance, issues that had not been previously explored. After introducing appropriate cost-sensitive evaluation measures, we reach the conclusion that additional safety nets are needed for the Naive Bayesian anti-spam filter to be viable in practice.<|reference_end|>
@article{androutsopoulos2000an,
    title={An evaluation of Naive Bayesian anti-spam filtering},
    author={Ion Androutsopoulos, John Koutsias, Konstantinos V. Chandrinos, George
  Paliouras and Constantine D. Spyropoulos},
    journal={Proceedings of the workshop on Machine Learning in the New
  Information Age, G. Potamias, V. Moustakis and M. van Someren (eds.), 11th
  European Conference on Machine Learning, Barcelona, Spain, pp. 9-17, 2000},
    year={2000},
    archivePrefix={arXiv},
    eprint={cs/0006013},
    primaryClass={cs.CL cs.AI}
}