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An Evaluation of Naive Bayesian Anti-Spam Filtering

By Ion Androutsopoulos, John Koutsias, Konstantinos V. Chandrinos, Konstantinos V. Ch, George Paliouras and Constantine D. Spyropoulos

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

Year: 2000
OAI identifier: oai:CiteSeerX.psu:10.1.1.19.6259
Provided by: CiteSeerX
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