Better Naive Bayes classification for high-precision spam detection

Yang Song, Aleksander Kołcz, C. Lee Gilez

Research output: Contribution to journalArticlepeer-review

35 Scopus citations


Email spam has become a major problem for Internet users and providers. One major obstacle to its eradication is that the potential solutions need to ensure a very low false-positive rate, which tends to be difficult in practice. We address the problem of low-FPR classification in the context of naive Bayes, which represents one of the most popular machine learning models applied in the spam filtering domain. Drawing from the recent extensions, we propose a new term weight aggregation function, which leads to markedly better results than the standard alternatives. We identify short instances as ones with disproportionally poor performance and counter this behavior with a collaborative filtering-based feature augmentation. Finally, we propose a tree-based classifier cascade for which decision thresholds of the leaf nodes are jointly optimized for the best overall performance. These improvements, both individually and in aggregate, lead to substantially better detection rate of precision when compared with some of the best variants of naive Bayes proposed to date.

Original languageEnglish (US)
Pages (from-to)1003-1024
Number of pages22
JournalSoftware - Practice and Experience
Issue number11
StatePublished - Aug 10 2009

All Science Journal Classification (ASJC) codes

  • Software


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