Spam Filtering Using a Markov Random Field Model With Variable Weighting Schemas

This paper presents a Markov Random Field model based approach to filter spam. Their approach examines the importance of the neighborhood relationship (MRF cliques) among words in an email message for the purpose of spam classification. They propose and test several different theoretical bases for weighting schemes among corresponding neighborhood windows. Their results demonstrate that unexpected side effects depending on the neighborhood window size may have larger accuracy impact than the neighborhood relationship effects of the Markov Random Field.