One-class support vector machines with a conformai kernel. A case study in handling class imbalance

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3138, Issue , 2004, Pages 850-858

  Cohen, Gilles ^a,b   Hilario, Mélanie ^b   Pellegrini, Christian ^b  

^a GENEVA UNIVERSITY HOSPITALS   (Switzerland)

^b UNIVERSITY OF GENEVA   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER AIDED DIAGNOSIS; DIAGNOSIS; PATIENT TREATMENT; PATTERN RECOGNITION; TEXT PROCESSING;

CLASS IMBALANCE; CLASSIFICATION TASKS; CONCOMITANT DECREASE; INFECTED PATIENTS; NOSOCOMIAL INFECTION; ONE-CLASS SUPPORT VECTOR MACHINE; ONE-CLASS SVMS; TEXT CATEGORIZATION;

SUPPORT VECTOR MACHINES;

EID: 35048860377     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-27868-9_93     Document Type: Article

References (18)

1. K. All, S. Manganaris, and R. Srikant. Partial classification using association rules. In Proc. 3rd International Conference on Knowledge Discovery in Databases and Data Mining, 1997.
2. S. Amari and S. Wu. Improving support vector machine classifiers by modifying kernel functions. Neural Networks, 12(6):783-789, 1999.
3. S. Amari and S. Wu. An information-geometrical method for improving the performance of support vector machine classifiers. In ICANN99, pages 85-90, 1999.
4. C. Bishop. Novelty detection and neural network validation. IEEE Proceedings on Vision, Image and Signal Processing, 141(4):217-222, 1994.
5. W.M. Boothby. An introduction to differential manifolds and Riemannian geometry. Academic Press, Orlando, 1986.
6. C. Burges. Geometry and invariance in kernel based methods. In MIT Press, editor, Adv. in kernel methods: Support vector learning, 1999.
7. G. Cohen, M. Hilario, H. Sax, and S. Hugonnet. Asymmetrical margin approach to surveillance of nosocomial infections using support vector classification. In Intelligent Data Analysis in Medicine and Pharmacology, 2003.
8. C. Cortes and V. Vapnik. Support vector networks. Machine Learning, 20(3) :273-297, September 1995.
9. N. Cristianini and Taylor J.S. An Introduction to Support Vector Machines. Cambridge University Press, 2000.
10. P. Domingos. A general method for making classifiers cost-sensitive. In Proc. 5th International Conference on Knowledge Discovery and Data Mining, pages 155-164, 1999.
11. U.R. Fletcher. Practical Methods of Optimization. John Wiley and Sons, 1987.
12. G. G. French, A. F. Cheng, S. L. Wong, and S. Donnan. Repeated prevalence surveys for monitoring effectiveness of hospital infection control. Lancet, 2:102-123, 1983.
13. S. Harbarth, Ch. Ruef, P. Francioli, A. Widmer, D. Pittet, and Swiss-Noso Network. Nosocomial infections in Swiss university hospitals: a multi-centre survey and review of the published experience. Schweiz Med Wochenschr, 129:1521-28, 1999.
14. N. Japkowicz. The class imbalance problem: A systematic study. Intelligent Data Analysis Journal, 6(5), 2002.
15. M. Kubat and S. Matwin. Addressing the curse of imbalanced data sets: Onesided sampling. In Procsȯf the Fourteenth International Conference on Machine Learning, pages 179-186, 1997.
16. B. Scholkopf, R.C. Williamson, A.J. Smola, J. Shawe-Taylor, and J.Platt. Estimating the support of a high-dimensional distribution. In Neural Computation, volume 13, pages 1443-1471. MIT Press, 1999.
17. L. Tarassenko, P. Hayton, N. Cerneaz, and M. Brady. Novelty detection for the identification of masses in mammograms. In Proceedings of the 4th IEE International Conference on Artificial Neural Networks (ICANN'95), pages 442 - 447, 1995.
18. V. Vapnik. Statistical Learning Theory. Wiley, 1998.