Training SVM email classifiers using very large imbalanced dataset

Journal of Experimental and Theoretical Artificial Intelligence

Volumn 24, Issue 2, 2012, Pages 193-210

  Diao, Lili ^a   Yang, Chengzhong ^b   Wang, Hao ^c  

^a TREND MICRO INC   (United States)

^b NANJING UNIVERSITY   (China)

^c UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

email classification; imbalance learning; support vector machine; training set compression; undersampling

Indexed keywords

ANTI-SPAM; CONVENTIONAL TECHNOLOGY; DATA SAMPLE; EMAIL CLASSIFICATION; ENTERPRISE APPLICATIONS; HONEYPOTS; IMBALANCE LEARNING; IMBALANCED DATASET; INFORMATION LOSS; MINIMAL INFORMATION; SIMILARITY MEASURE; SPAM EMAILS; SUPPORT VECTOR MACHINE (SVM); SVM CLASSIFIERS; TRAINING DATA; TRAINING SETS; UNDER-SAMPLING;

CLASSIFICATION (OF INFORMATION); ELECTRONIC MAIL; INTERNET; PROBLEM SOLVING;

SUPPORT VECTOR MACHINES;

EID: 84859174817     PISSN: 0952813X     EISSN: 13623079     Source Type: Journal    
DOI: 10.1080/0952813X.2011.610033     Document Type: Article

References (32)

1. Akbani, R. Kwek, S., and Japkowicz, N. (2004), Applying Support Vector Machines to Imbalanced Datasets, in Proceedings of the 15th European Conference on Machine Learning (ECML), 20-24 September, Pisa, Italy, pp. 39-50 (Pubitemid 41050079)
2. Anti-Spam Center of ISC. (2008), 4Q Anti-spam Investigation Report, In Chinese
3. Baszczynski, J. Deckert, M., Stefanowski, J., and Wilk, S. (2010), Integrating Selective Pre-processing of Imbalanced Data with Ivotes Ensemble, in Proceedings of the 7th International Conference on Rough Sets and Current Trends in Computing, 28-30 June, Warsaw, Poland, pp. 148-157
4. Boser, BE. Guyon, I.M., and Vapnik, V.N. (1992), A Training Algorithm for Optimal Margin Classifiers, in Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory, 27-29 July, Pittsburgh, PA, pp. 144-152
5. Carreras, X. and Marquez, L. (2001), Boosting Trees for Anti-spam Email Filtering, in Proceedings of the European Conference Recent Advances Natural Language Processing, 5-7 September, Tzigov Chark, Bulgaria, pp. 58-64
6. Cauwenberghs, G. and Poggio, T. (2001), Incremental and Decremental Support Vector Machine Learning, in Proceedings of the Advances in Neural Information Processing Systems (NIPS), Denver, CO, USA, pp. 409-415
7. Chawla, NV, Bowyer, KW, Hall, LO, and, Kegelmeyer, WP. 2002. SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research (JAIR), 16: 321-357. (Pubitemid 43057176)
8. Cristianini, N, and, Shawe-Talyor, J. 2005. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, 1st, Beijing, China: China Machine Press.
9. Diehl, CP. and Cauwenberghs, G. (2003), SVM Incremental Learning, Adaptation and Optimization, in Proceedings of the International Joint Conference on Neural Networks, 20-24 July, Portland, OR, pp. 2685-2690
10. Drucker, H, Wu, D, and, Vapnik, VN. 1999. Support Vector Machines for Spam Categorization. IEEE Transactions on Neural Networks, 20: 1048-1054.
11. Fallows, D. 2003. Spam: How it is Hurting Email and Degrading Life on the Internet, Pew Internet and American Life Project (PIP), Washington, DC: PEW Research Center.
12. Fung, G. and Mangasarian, O.L. (2002), Incremental Support Vector Machine Classification, in Proceedings of the 2nd SIAM International Conference on Data Mining (SDM), 11-13 April, Arlington, VA, pp. 247-260
13. Han, J, and, Kamber, M. 2001. Data Mining: Concepts and Technologies, Beijing, China: Higher Education Press and Morgan Kaufmann Publishers.
14. Joachims, T. 1999. Making Large-scale SVM Learning Practical. In Advances in Kernel Methods-Support Vector Learning, Edited by: Schlkopf, B, Burges, C and Smola, A. 169-184. Cambridge, MA: MIT Press.
15. Joachims, T. (2006), Training Linear SVMs in Linear Time, in Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 20-23 August, Philadelphia, PA, pp. 217-226 (Pubitemid 44535518)
16. Jones, S, and, Fox, S. 2009. Generations Online in January 2009, Pew Internet and American Life Project (PIP), Washington, DC: PEW Research Center.
17. Kubat, M. and Matwin, S. (1997), Addressing the Curse of Imbalanced Training Sets: One-sided Selection, in Proceedings of the 14th International Conference on Machine Learning (ICML), 8-12 July, Nashville, TN, pp. 179-186
18. Osuna, E. Freund, R., and Girosi, F. (1997), An Improved Training Algorithm for Support Vector Machines, in Proceedings of the 1997 IEEE Workshop, 24-26 September, Amelia Island, FL, pp. 276-285
19. Platt, JC. (1998), Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines, Technical Report MSR-TR-98-14, Microsoft Research
20. Raghavan, P. (1997), Information Retrieval Algorithms: A Survey, in Proceedings of the 8th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), 5-7 January, Philadelphia, PA, pp. 11-18
21. Raskutti, B, and, Kowalczyk, A. 2004. Extreme Rebalancing for SVMs: A Case Study. SIGKDD Explorations, 6: 60-69.
22. Sahami, M. Dumais, S., Heckerman, D., and Horvitz, E. (1998), A Bayesian Approach to Filtering Junk E-mail, in Proceedings of the AAAI Workshop Learning for Text Categorization, 26-30 July, Madison, WI, pp. 55-62
23. Stefanowski, J. and Wilk, S. (2008), Selective Pre-processing of Imbalanced Data for Improving Classification Performance, in Proceedings of the 10th International Conference on Data Warehousing Knowledge Discovery, 2-5 September, Turin, Italy, pp. 283-292
24. Syed, NA. Liu, H., and Sung, K. (1999), Incremental Learning with Support Vector Machines, in Proceedings of the IJCAI99 Workshop on Support Vector Machines, 31 July-6 August, Stockholm, Sweden
25. Tang, Y, Zhang, YQ, Chawla, NV, and, Krasser, S. 2009. SVMs Modeling for Highly Imbalanced Classification. IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics, 39: 281-288.
26. Vapnik, VN. 1998. Statistical Learning Theory, New York, NY: John Wiley and Sons Inc.
27. Vapnik, VN. 1995. The Nature of Statistical Learning Theory, Berlin, German: Springer-Verlag.
28. Wang, BX, and, Japkowicz, N. 2010. Boosting Support Vector Machines for Imbalanced Data Sets. Knowledge and Information Systems, 25: 1-20.
29. Wikipedia. (2009), Spamtrap, http://en.wikipedia.org/wiki/Spamtrap
30. Wu, G. and Chang, E.Y. (2003), Class-boundary Alignment for Imbalanced Dataset Learning, in Proceedings of the 20th ICML Workshop on Learning from Imbalanced Datasets, 21-24 August, Washington, DC, pp. 49-56
31. Wu, G, and, Chang, EY. 2005. KBA: Kernel Boundary Alignment Considering Imbalanced Data Distribution. IEEE Transactions on Knowledge and Data Engineering (TKDE), 17: 786-795. (Pubitemid 40860458)
32. Yan, R. Liu, Y., Jin, R., and Hauptmann, A. (2003), On Predicting Rare Classes with SVM Ensembles in Scene Classification, in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, 6-10 April, Hong Kong, pp. 21-24