Yıl 2012,
Cilt: 2 Sayı: 1, 72 - 78, 01.06.2012
Öz
This paper proposes the performance of Genetic Programming (GP) methods on Wisconsin breast cancer data. The Wisconsin Diagnostic Breast Cancer (WDBC) dataset, provided by the University of Wisconsin Hospital, was derived from a group of images using Fine Needle Aspiration (FNA) of the breast. Genetic Programming with different population size was employed to this study. Therefore, GP was trained with 50, 100 and 200 population size and ten-folds cross validation procedure. Results showed %96.6 success rate on 50 population with GP
Anahtar Kelimeler
Kaynakça
- http://www.imagins.com/breast health_cancer.asp. Street, W., Wolberg, W. and Mangasarian, O. , “Machine Learning Techniques to Diagnose Breast Cancer from Image-Processed Nuclear Features of Fine Needle Aspirates”, Cancer Letters Vol. 77 pages:163-171, 1994
- P. E. H. R. O. Duda and D. G. Stock, editors. Pattern ClassiŞcation, Second Edition. JohnWiley & sons,New york, 2001.
- Furundzic D., Djordjevic, and Bekic A. J., Neural Networks approach to early breast cancer detection. Systems Architecture, 44:617-633, 1998.
- J. S.-T. N. Cristianini, An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press, 2000.
- C. Hsu and C. Lin, A comparison of methods for multi-class support vector machines. IEEE Transactions on Neural Networks, (13):415–425, J. R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge, MA: MIT Press, 1992.
- J.R. Koza, F.H. Bennett III, D. Andre, M.A. Keane, Kaufmann Publ. Inc., 1999.
- Tsakonas A. “A comparison of classification accuracy of four genetic programming-evolved intelligent structures” Information (6):691-724, 2006 Sciences.
- Wolberg W. H. and Mangasarian O. L., Multisurface method of pattern separation for medical diagnosis applied to breast cytology, in: Proceedings of the USA National Academy of Sciences 87, pp. 91939196, 1990
Yıl 2012,
Cilt: 2 Sayı: 1, 72 - 78, 01.06.2012
Öz
Kaynakça
- http://www.imagins.com/breast health_cancer.asp. Street, W., Wolberg, W. and Mangasarian, O. , “Machine Learning Techniques to Diagnose Breast Cancer from Image-Processed Nuclear Features of Fine Needle Aspirates”, Cancer Letters Vol. 77 pages:163-171, 1994
- P. E. H. R. O. Duda and D. G. Stock, editors. Pattern ClassiŞcation, Second Edition. JohnWiley & sons,New york, 2001.
- Furundzic D., Djordjevic, and Bekic A. J., Neural Networks approach to early breast cancer detection. Systems Architecture, 44:617-633, 1998.
- J. S.-T. N. Cristianini, An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press, 2000.
- C. Hsu and C. Lin, A comparison of methods for multi-class support vector machines. IEEE Transactions on Neural Networks, (13):415–425, J. R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge, MA: MIT Press, 1992.
- J.R. Koza, F.H. Bennett III, D. Andre, M.A. Keane, Kaufmann Publ. Inc., 1999.
- Tsakonas A. “A comparison of classification accuracy of four genetic programming-evolved intelligent structures” Information (6):691-724, 2006 Sciences.
- Wolberg W. H. and Mangasarian O. L., Multisurface method of pattern separation for medical diagnosis applied to breast cytology, in: Proceedings of the USA National Academy of Sciences 87, pp. 91939196, 1990
Toplam 8 adet kaynakça vardır.
Ayrıntılar
| Diğer ID | JA56BN24AC |
|---|---|
| Bölüm | Makaleler |
| Yazarlar | |
| Yayımlanma Tarihi | 1 Haziran 2012 |
| Yayımlandığı Sayı | Yıl 2012 Cilt: 2 Sayı: 1 |