Granular support vector machines using linear decision hyperplanes for fast medical binary classification

IEEE International Conference on Fuzzy Systems

Volumn , Issue , 2005, Pages 138-142

  Tang, Yuchun ^a   Jin, Bo ^a   Zhang, Yan Qing ^a   Fang, Hao ^a   Wang, Binghe ^a  

^a GEORGIA STATE UNIVERSITY   (United States)

Author keywords

Binary Classification; Granular Computing; Granular Support Vector Machines; Maximal Margin Principle; Principle Component Analysis

Indexed keywords

COMPUTATION THEORY; DATABASE SYSTEMS; DECISION THEORY; MATHEMATICAL MODELS; OPTIMIZATION; PRINCIPAL COMPONENT ANALYSIS; ROBUSTNESS (CONTROL SYSTEMS);

BINARY CLASSIFICATION; GRANULAR COMPUTING; GRANULAR SUPPORT VECTOR MACHINES; MAXIMAL MARGIN PRINCIPLE;

CLASSIFICATION (OF INFORMATION);

EID: 23944502099     PISSN: 10987584     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. D. Hand, H. Mannila, P. Smyth, Principle of Data Mining, The MIT Press, Cambridge, London, 2001.
2. R.A. Fisher, "The Use of Multiple Measurements in Taxonomic Problems," Annals of Eugenics, vol. 7, part II, pp. 179-188, 1936.
3. V. Vapnik, Statistical Learning Theory, New York, John Wiley and Sons, 1998.
4. N. Cristianini, J,Shawe-Taylor, An introduction to support Vector Machines and other kernel-based learning methods, Cambridge University Press, New York, NY, 1999
5. C. J. C. Burges, "A tutorial on support vector machines for pattern recognition," Data Mining and Knowledge Discovery, 2(2), pp. 121-167, 1998.
6. K. K. Chin, "Support vector machines applied to speech pattern classification," Master's thesis, Engineering Department, Cambridge University, 1999.
7. S. Gunn, "Support vector machines for classification and regression," ISIS technical report, Image Speech & Intelligent Systems Group, University of Southampton, 1998.
8. A. Bargiela, Granular Computing: An Introduction, Kluwer Academic Pub, Kluwer, 2002.
9. J. T. Yao, Y. Y. Yao, "A granular computing approach to machine learning," Proceedings of FSKD'02, Singapore, pp732-736, 2002.
10. Y.Y. Yao "On Modeling data mining with granular computing," Proceedings of COMPSAC 2001, pp.638-643, 2001.
11. Y. -Q. Zhang, M. D. Fraser, R. A. Gagliano, A. Kandel, "Granular neural networks for numerical-linguistic data fusion and knowledge discovery," IEEE Transactions on Neural Networks, vol.11, no. 3, pp. 658-667, 2000.
12. K. P. Bennett, J. Blue, "A support vector machine approach to decision trees," R.P.I math report no. 97-100, Rensselaer Polytechnic Institute, Troy, NY, 1997.
13. H. Yu, J. Yang and J. Han, "Classifying large data sets using SVMs with hierarchical clusters," Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, ACM Press, Washington, D.C., 2003
14. Y.C. Tang, B. Jin, Y. Sun, Y.-Q. Zhang, "Granular Support Vector Machines for Medical Binary Classification Problems, " Proc. of IEEE CIBIB 2004, San Diego, Oct. 7-8, 2004
15. C.-W. Hsu, C.-C. Chang, C.-J. Lin, "A practical guide to support vector classification", Available at http://www.csie.ntu.edu.tw/~cjlin/ papers/guide/guide.pdf.
16. J. Ma, Y. Zhao, and S. Ahalt, OSU SVM Classifier Matlab Toolbox, Available at http://www.ece.osu.edu/~maj/osu_svm/.
17. C. -C. Chang, C. -J. Lin, LIBSVM: a alibrary for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
18. C.J. Merz and P.M. Murphy. UCI repository of machine learning databases, 1998. http://www.ics.uci.edu/~mlearn/MLRepository.html.