Asymmetric support vector machines: Low false-positive learning under the user tolerance

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2008, Pages 749-757

  Wu, Shan Hung ^a,b   Lin, Keng Pei ^a   Chen, Chung Min ^b   Chen, Ming Syan ^a  

^a NATIONAL TAIWAN UNIVERSITY   (Taiwan)

^b Telcordia Applied Research Center   (Taiwan)

Author keywords

Classification; Low false positive learning; Support Vector Machine (SVM)

Indexed keywords

CLASSIFICATION; EXTENSIVE SIMULATIONS; HIGH-DIMENSIONAL DATUM; LOW FALSE-POSITIVE LEARNING; SEQUENTIAL MINIMAL OPTIMIZATIONS; SUPPORT VECTOR MACHINE (SVM); TRAINING TIME; USER TOLERANCES;

CLUSTERING ALGORITHMS; EDUCATION; GEARS; IMAGE RETRIEVAL; INFORMATION MANAGEMENT; MINING; MULTILAYER NEURAL NETWORKS; VECTORS;

SUPPORT VECTOR MACHINES;

EID: 65449163898     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1401890.1401980     Document Type: Conference Paper

References (33)

1. I. Androutsopoulos, J. Koutsias, K. Chandrinos, and C. Spyropoulos. An experimental comparison of naive bayesian and keyword-based anti-spam filtering with personal e-mail messages. In Proc. of SIGIR, 2000.
2. A. Asuncion and D.J. Newman. UCI Machine Learning Repository, 2007.
3. D. Barbara, N. Wu, and S. Jajodia. Detecting novel network intrusions using bayes estimators. In Proc. of the 1st SIAM Conference on Data Mining (SDM), 2001.
4. P. Bartlett and J. Shawe-Taylor. Generalization performance of support vector machines and other pattern classifiers. In Advances in Kernel Methods: Support Vector Learning. MIT Press, 1998.
5. A. Ben-Hur, D. Horn, H.T. Siegelmann, and V. Vapnik. Support vector clustering. Journal of Machine Learning Research, 2:125-137, 2001.
6. P. Boykin and V. Roychowdhury. Leveraging social networks to fight spam. IEEE Computer, 2005.
7. A. Bratko, G. Cormack, B. Filipic, T. Lynam, and B. Zupan. Spam, filtering using statistical data compression, models. Journal of Machine Learning Research, 7, 2006.
8. L. Breiman. Classification and Regression Trees. Chapman & Hall, 1998.
9. C. Burges. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2): 121-167, 1998.
10. X. Carreras and L. Marquez. Boosting trees for anti-spam email fltering. In Proc. of the 4th International Conference on Recent Advances in Natural Language Processing, 2001.
11. C.-C. Chang and C.-J. Lin. LIBSVM: a library for support vector machines. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm, 2001.
12. H.D. Cheng, X. Cai, X. Chen, L. Hu, and X. Lou. Computer-aided detection and classification of microcalcifications in mammograms: a survey. Pattern Recognition, 36(12):2967-2991, 2003.
13. G. Cormack and T. Lynam. Overview of the trec 2005 spam evaluation track. In Fourteenth Text REtrieval Conference (TREC-2005). NIST, 2005.
14. C. Cortes and V. Vapnik. Support vector networks. Machine Learning, 20:273-297, 1995.
15. H. Drucker, D. Wu, and V. Vapnik. Support vector machines for spam categorization. IEEE Transactions on Neural networks, 10(5), 1999.
16. J. Goodman, G. Cormack, and D. Heckerman. Spam and the ongoing battle for the inbox. Communications of the ACM, 50(2):24-33, February 2007.
17. C.-W. Hsu, C.-C. Chang, and C.-J. Lin. A practical guide to support vector classification. Technical report, http://www.csie.ntu.edu.tw/~cjlin/ libsvm, 2003.
18. J. Kivinen, A. Smola, and R. Williamson. Online learning with kernels. Advances in Neural Information Processing Systems. MIT Press, 14:785-793, 2002.
19. A. Kolcz and J. Alspector. SVM-based filtering of e-mail spam with content-specific misclassification costs. In Proc. of TextDM, 2001.
20. H.-Y. Lam and D.-Y. Yeung. A learning approach to spam detection based on social networks. In Proc. of the 4th Conference on Email and Anti-Spam (CEAS), 2007.
21. T. Lynam, G. Cormack, and D. Cheriton. On-line spam filter fusion. In Proc. of SIGIR, pages 123-130, 2006.
22. J. Platt. Sequential minimal optimization: A fast algorithm for training support vector machines. In Advances in Kernel Methods: Support Vector Learning. MIT Press, 1998.
23. D. Prokhorov. IJCNN 2001 neural network competition, 2001. Slide presentation in IJCNN'01, Ford Research Laboratory.
24. M. Sahami, S. Dumais, D. Heckerman, and E. Horvitz. A bayesian approach to filtering junk e-mail. In AAAI Technical Report WS-98-05, 1998.
25. K. Schneider. A comparison of event models for naive bayes anti-spam e-mail filtering. In Proc. of the 11th Conference of the European Chapter of the Association for Computational Linguistics, 2003.
26. B. Scholkopf, J. Platt, J. Shawe-Taylor, A. Smola, and R. C.Williamson. Estimating the support of a high-dimensional distribution. Neural Computation, 13:1443-1471, 2001.
27. B. Scholkopf and A. Smola. Learning with Kernels:: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, 2002.
28. D. Sculley and G. Wachman. Relaxed online support vector machines for spam filtering. In Proc. of SIGIR, 2007.
29. J. Shawe-Taylor, P.L. Bartlett, R.C. Williamson, and M. Anthony. Structural risk minimization over data-dependent hierarchies. IEEE Transactions on Information Theory, 44(5): 1926-1940, 1998.
30. V. Vapnik. Statistical Learning Theory. Wiley, NY, 1998.
31. P. Viola and M. Jones. Fast and robust classification using asymmetric adaboost and a detector cascade. In Proc. of Neural Information Processing Systems (NIPS), 2002.
32. W. Yih, J. Goodman, and G. Hulten. Learning at low false positive rates. In Proc. of the 3rd Conference on Email and Anti-Spam (CEAS), 2006.
33. B. Zheng, W Qian, and L.P. Clarke. Digital mammography: mixed feature neural network with spectralentropy decision for detection of microcalcifications. IEEE Transactions on Medical Imaging, 15(5):589-597, 1996.