A new descriptor selection scheme for SVM in unbalanced class problem: A case study using skin sensitisation dataset

SAR and QSAR in Environmental Research

Volumn 18, Issue 5-6, 2007, Pages 423-441

  Li, S ^a,b   Fedorowicz, A ^a   Andrew, M E ^a  

^a NATIONAL INSTITUTE FOR OCCUPATIONAL SAFETY AND HEALTH   (United States)

^b WEST VIRGINIA UNIVERSITY   (United States)

Author keywords

Fisher's linear discriminant analysis; Skin sensitisation; Support vector machine; Unbalanced data; Variable selection

Indexed keywords

DISCRIMINANT ANALYSIS; FORESTRY; RADIAL BASIS FUNCTION NETWORKS;

BACKWARD ELIMINATION; DESCRIPTORS; FISHERS'S LINEAR DISCRIMINANT ANALYSIS; RANDOM FORESTS; SELECTION SCHEME; SKIN SENSITIZATION; SUPPORT VECTORS MACHINE; T-TESTS; UNBALANCED DATA; VARIABLES SELECTIONS;

SUPPORT VECTOR MACHINES;

EID: 34547546371     PISSN: 1062936X     EISSN: 1029046X     Source Type: Journal    
DOI: 10.1080/10629360701428474     Document Type: Article

References (28)

1. C.J.C. Burges. Data Mining Knowledge Discovery, 2, 2 (1998).
2. K. Bennett, C. Campbell. SIGKDD Explorations 2, 2 (2000).
3. C. Cortes, V.N. Vapnik. Mach. Learn, 20, 3 (1995).
4. N. Cristianini, J. Shawe-Taylor. An introduction to Support Vector Machines and Other Kernel-based Learning Methods, Cambridge University Press, Cambridge (2000).
5. V.N. Vapnik. Statistical Learning Theory, John Wiley & Sons, New York (1998).
6. A.J. Smola. Learning with Kernels, PhD thesis, Technische Universität Berlin (1998).
7. R. Kohavi, G.H. John. Artif. Intell., 97, 1 (1997).
8. D.C. Whitley, M.G. Ford, D.J. Livingstone. J. Chem, Inf. Comput. Sci., 40, 5 (2000).
9. M. Hall. Correlation-based Feature Subset Selection for Machine Learning. Ph.D. thesis, Department of Computer Science, University of Waikato (1999).
10. J. Biesiada and W. Duch. Paper presented at the Proceedings of the 4th International Conference on Computer Recognition Systems CORES'05, Springer (2005).
11. J.-H. Lee, C.-J. Lin. Automatic model selection for support vector machines (2000). Available online at: http://www.csie.ntu.edu.tw/~cjlin/papers/ modelselect.ps.gz
12. I. Guyon, J. Weston, S. Barnhill. Mach. Learn., 46, 1 (2002).
13. H, Fröhlich, A. Zell. Paper presented at the 2004 IEEE International Joint Conference on Neural Networks, IEEE (2004).
14. J. Weston, S. Mukherjee, O. Chapelle, M. Pontil, T. Poggio, V. Vapnik. in Avance in Neural Information Processing Systems 13, pp. 668-674, MIT Press, Cambridge, MA (2001).
15. Y. Grandvalet, S. Canu. Advance in Neural Information Processing Systems 15, MIT Press, Cambridge, MA (2003).
16. O. Chapelle, V. Vapnik. Advance in Neural Information Processing Systems 12, MIT Press, Cambridge, MA (2000).
17. J.J. Chen, C.A. Tsai, J.F. Young, R.L. Kodell. SAR QSAR Environ. Res., 16, 6 (2005).
18. N. Japkowicz. Paper presented at the Proceedings of the 2000 International Conference on Artificial Intelligence, Las Vegas, NV (2000).
19. N.A. Chawla, K.W. Bowyer, L.O. Hall, W.P. Kegelmeyer. J. Artif. Intell Res. (JAIR), 16, 1 (2002).
20. A. Nickerson, N. Japkowicz, E. Milios. Paper presented at the Proceedings of Eighth International Workshop on AI and Statistics, Key West, FL (2001).
21. C.K. Smith, S.A.M. Hotchkiss. Allergic Contact Dermatitis, Taylor & Francis, New York (2001).
22. K.E. Haneke, R.R. Tice, B.L. Carson, B.H. Margolin, W.S. Stokes. Regul Toxicol. Pharmacol., 34, 3 (2001).
23. NIH. NIH Publication No. 99-4494: The Murine Local Lymph Node Assay: A Test Method for Assessing the Allergic Contact Dermatitis Potential of Chemicals/Compounds, NIH, Bethesda, MD (1999).
24. A. Fedorowicz, H. Singh, S. Soderholm, E. Demchuck. Chem. Res. Toxicol., 18, 6 (2005).
25. D.A. Basketter, Z.M. Wright, E.V. Warbrick, R.J. Dearman, I. Kimber, C.A. Ryan, G.F. Gerberick, I.R. White. Contact Dermatitis, 45, 2 (2001).
26. L. Breiman. Mach. Learn., 45, 1 (2001).
27. S. Li, A. Fedorowicz, H. Singh, S.C. Soderholm. J. Chem. Inf. Model., 45, 4 (2005).
28. W.N. Venables, B.D. Ripley. Modern Applied Statistics with S-SPLUS, Springer, New York (1999).