An evaluation of Naive Bayesian anti-spam filtering

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.

9 pages

References in corpus (2)

• Machine Learning in Automated Text Categorization
• Mistake-Driven Learning in Text Categorization

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• Modelling Cyber-Security Experts' Decision Making Processes using Aggregation Operators