Breast cancer diagnosis via supp ort vector machines

2006 Chinese Control Conference Proceedings, CCC 2006

Volumn , Issue , 2006, Pages 1853-1856

  Wang, Yi ^a   Wan, Fuyong ^a  

^a EAST CHINA NORMAL UNIVERSITY   (China)

Author keywords

Breast cancer diagnosis; Cross entropy filter; Generalization bound; Non symmetrical C SVM

Indexed keywords

PARAMETER ESTIMATION; PROBLEM SOLVING; SUPPORT VECTOR MACHINES; TUMORS;

BREAST CANCER DIAGNOSIS; CROSS-ENTROPY FILTER; GENERALIZATION BOUND;

DIAGNOSIS;

EID: 37349061665     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CHICC.2006.280871     Document Type: Conference Paper

References (22)

1. A. R. Webb, Statistical Pattern Recognition, Wiley, 2002.
2. C. F. Aliferis, I. Tsamardinos, A Statnikov, A novel Markov blanket algorithm for optimal variable selection, Technical Report DSL, AMIA, 2003.
3. D. Koller, M. Sahami, Toward Optimal Feature Selection, Proceedings of the 13th International Conference on Machine Learning (ML), 284-292, 1996.
4. D. W. Aha, R. L. Bankert, A comparative evaluation of sequential feature selection algorithms, Proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, 1-7, 1995.
5. E. Marshall, Search for a killer: Focus shifts from fat to hormones in special report on breast cancer, Science, Vol.259, 618-621, 1993.
6. G. John, R. Kohavi, K. Pfleger, Irrelevant features and the subset selection problem, Machine Learning: Proceedings of the Eleventh Inernational Conference, Morgan Kaufmann, 121-129, 1994.
7. H. Almauallium, T. G. Dietterich, Learning with many Irrelevant Features, Proceedings of Ninth National Conference on Artificial Intelligence (UAI'94), Vol.2, 547-552, 1991.
8. Hussein A. Abbass, An evolutionary artificial neural network approach for breast cancer diagnosis, Accepted to appear in Artificial Intelligence in Medicine, 2002
9. H. H. Zhang, J. Ahn, X. Lin, C. Park, Gene selection using support vector machines with nonconvex penalty, Bioinformatics Advance Access, published October 25, 2005.
10. J. Pearl, Probabilistic Reasoning in Intelligent Systems, Morgan Kaufmann, 1988.
11. Kononenko, Igor, Estimating attributes: analysis and extensions of RELIEF , European Conference on Machine Learning, 171-182, 1994.
12. K. Rose, Extracting rules from pruned neural networks for breast cancer diagnosis, Artificial Intelligence in Medicine, IEEE Press, 1760-1767, 1999.
13. L. Breiman, J. Friedman, R Olshen, C. Stone, Classification and Regression Trees, Wadsworth, 1984.
14. N. Ancona, G. Cicirelli, E. Stella, A, Distante, Ball detection in static images with Support Vector Machines for classification, Proceedings ICANN'95-International Conference on Artificial Neural Network, Vol.2, 53-60, 1995.
15. N. Cristianini, J. Shawe-Taylor, An Introduction to Support Vector Machines and other kernel-based learning methods, Cambridge University Press, 2000.
16. Olvi L. Mangasarian and W. Nick Street and William H. Wolberg, Breast cancer diagnosis and prognosis via linear programming, Operations Research, Vol. 43, 570-577, 1995.
17. T. Joachims, Learning to Classify Text Using Support Vector Machines: Methods, Theory, and Algorithms, Kluwer, 2002.
18. V. Vapnik, The Nature of Statistical Learning Theory, Springer Verlag, 1995.
19. W. J. Frable, Thin-needle aspiration biopsy, Major Problems in Pathology, W. B. Suanders Co., Philadelphia, 1983.
20. W. N. Street, Cancer Diagnosis and Prognosis via Linear-Programming-Based Machine Learning, PhD thesis, University of Wisconsin-Madison, August 1994.
21. Xin Yao, Yong Liu, Neural networks for breast cancer diagnosis, Proceedings of the Congress on Evolutionary Computation, Vol.8, 37-51, 1996.
22. Y. LeCun, Comparison of learning algorithms for handwritten digit recognition, Image and Vision Computing, Vol.21, 675-692, 2003.