An evaluation of Naive Bayes variants in content-based learning for spam filtering

Intelligent Data Analysis

Volumn 11, Issue 5, 2007, Pages 497-524

  Seewald, Alexander K ^a  

^a Seewald Solutions   (Austria)

Author keywords

Empirical study; Machine learning; Naive Bayes; Spam filtering

Indexed keywords

EID: 36549012987     PISSN: 1088467X     EISSN: 15714128     Source Type: Journal    
DOI: 10.3233/ida-2007-11505     Document Type: Article

References (17)

1. I. Androutsopoulos, J. Koutsias, K.V. Chandrinos, G. Paliouras and C.D. Spyropoulos, An Evaluation of Naive Bayesian Anti-Spam Filtering, in: Proceedings of the Workshop on Machine Learning in the New Information Age, G. Potamias, V. Moustakis and M.N. van Someren, eds, 11th European Conference on Machine Learning, Barcelona, Spain, 2000, pp. 9-17.
2. G. Cormack and T. Lynam, A Study of Supervised Spam Detection Applied to Eight Months of Personal Email, http://plg.uwaterloo.ca/-gvcormac/ spamcormack.html, July 2004. To be published in a revised and extended version in ACM Transactions on Information Systems, 2006.
3. H. Drucker, V. Vapnik and D. Wu, Support vector machines for spam categorization, IEEE Transactions on Neural Networks 10(5) (1999), 1048-1054.
4. P. Graham, A Plan For Spam, www.paulgraham.com/spam.html, 2003.
5. J.M. Gómez Hidalgo, M. Maña López and E.P. Sanz, Combining Text and Heuristics for Cost-Sensitive Spam Filtering, in: Proceedings of the Fourth Computational Natural Language Learning Workshop, CoNLL-2000, Association for Computational Linguistics, Lisbon, Portugal, 2000.
6. P. Hoffman and D. Crocker, Unsolicited bulk email: Mechanisms for control, Technical Report UBE-SOL, IMCR-008, Internet Mail Consortium, 1998.
7. T.M. Meyer and B. Whateley, SpamBayes: Effective Open-Source, Bayesian Based, Email Classification System, in: Proceedings of the First Conference on EMail and Anti-Spam (CEAS), Mountain View, California, United States, 2004.
8. J. Platt, Fast Training of Support Vector Machines using Sequential Minimal Optimization, in: Advances in Kernel Methods - Support Vector Learning, B. Schlkopf, C. Burges and A. Smola, eds, MIT Press, 1998.
9. G. Sakkis, I. Androutsopoulos, G. Paliouras, V. Karkaletsis, C.D. Spyropoulos and P. Stamatopoulos, Stacking Classifiers for Anti-Spam Filtering of E-Mail, in: Proceedings of EMNLP-01, 6th Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Morristown, Pittsburgh, US, 2001.
10. G. Sakkis, I. Androutsopoulos, G. Paliouras, V. Karkaletsis, C.D. Spyropoulos and P. Stamatopoulos, A memory-based approach to anti-spam filtering for mailing lists, Information Retrieval Journal 6(1) (2003).
11. M. Sahami, S. Dumais, D. Heckerman and E. Horvitz, A Bayesian Approach to Filtering Junk e-mail, in: Learning for Text Categorization: Papers from the 1998 Workshop, Madison, Wisconsin, 1998. AAAI Technical Report WS-98-05.
12. A.K. Seewald and F. Kleedorfer, Lambda Pruning - An Approximation of the string subsequence kernel for practical SVM classification and redundancy clustering. Journal for Advances in Data Analysis and Classification, 3/2007, Springer.
13. A.K. Seewald, A Close Look at Current Approaches in Spam Filtering, Technical Report, Austrian Research Institute for Artificial Intelligence, Vienna, TR-2005-04, 2005.
14. A.K. Seewald, Combining Bayesian and Rule Score Learning: Automated Tuning for SpamAssassin, Technical Report, Austrian Research Institute for Artificial Intelligence, Vienna, TR-2004-11, 2004.
15. S. Gauthronet and E. Drouard, Unsolicited commercial communications and data protection, Technical report, Commission of the European Communities, 2001.
16. M. Sergeant, Internet Level Spam Detection and SpamAssassin 2.50, 2003 MIT Spam Conference, Cambridge, Massachusetts, USA, 2003. spamconference.org/ proceedings2003.html
17. W.S. Yerazunis, The Spam-Filtering Accuracy Plateau at 99.9% Accuracy and How to Get Past It, 2004 MIT Spam Conference, MIT, Cambridge, Massachusetts.