An Evaluation of Naive Bayesian Anti-Spam Filtering
by Ion Androutsopoulos, John Koutsias, Konstantinos V. Chandrinos, George Paliouras, Constantine D. Spyropoulos
arXiv.org e-Print archive,
2000-06-07
Language:
English
Note: 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
Abstract
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.
