Machine learning techniques have gained increasing demand in biomedical research due to capability of extracting complex relationships and correlations among members of the large data sets. Thus, over the past few decades, scientists have been concerned about computer information technology to provide computational learning methods for solving the complex medical problems. Support Vector Machine is an efficient classifier that is widely applied to biomedical and other disciplines. In recent years, new opportunities have been developed on improving Support Vector Machines’ classification efficiency by combining with any other statistical and computational methods. This study proposes a new method of Support Vector Machines for influential classification using combined kernel functions. The classification performance of the developed method, which is a type of non-linear classifier, was compared to the standart Support Vector Machine method by applying on seven different datasets of medical diseases. The results show that the new method provides a significant improvement in terms of the probability excess.
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Vapnik, V. N., The nature of statistical learning theory. Springer, NewYork, 2000.
DeCoste, D., and Schölkopf, B., Training invariant support vector machines. Machine Learning 46(1–3):161–190, 2002.
Schölkopf, B., Burges, C. J. C., Vapnik, V., Extracting support data for a given task, Proceedings, First International Conference on Knowledge Discovery and Data Mining, 252–257, Menlo Park, CA, 1995.
Joachims, T., Text categorization with support vector machine, In Proceedings of European Conference on Machine Learning (ECML), 1998.
Osuna, E., Freund, R., Girosi, F., Training support vector machines: An application to face detection, In Proceedings of the 1997 conference on Computer Vision and Pattern Recognition (CVPR’97), Puerto Rico, June 17–19, 1997.
Schölkopf, B., and Smola, A., Learning with Kernels. MIT Press, Cambridge, MA, 2002.
Müller, K. R., Mika, S., Rätsch, G., Tsuda, K., and Schölkopf, B., An introduction to kernel-based learning algorithms. IEEE Trans Neural Networks 12:181–201, 2001.
Schölkopf, B., Tsuda, K., Vert, J. P., Kernel methods in computational biology, MIT Press, Computational Molecular Biology. MIT Press, 2004.
Vert, J. P., Kernel methods in genomics and computational biology, Kernel Methods in Bioengineering, Signal and Image Processing, Idea Group, 42–63, 2007.
Huang, Y.-L., and D.-R., Chen, Support vector machines in sonography application to decision making in the diagnosis of breast cancer. Journal of Clinical Imaging 29:179–184, 2005.
Zhang, N., Ruan, S., Lebonvallet, S., Liao, Q., Zhu, Y., Multi-kernel SVM based classification for brain tumor segmentation of MRI multi-sequence, 16th IEEE International Conference on Image Processing, 3373–3376, 2009.
Bennett, K. P., and Blue, J. A., A support vector machine approach to decision trees. IEEE World Congress on Computational Intelligence 3:2396–2401, 1998.
Rakotomamonjy, A., Support vector machines and area under ROC curve. Technical report, University of Rouen, 2004.
Jayadeva, Khemchandani, R., and Chandra, S., Fast and robust learning through fuzzy linear proximal support vector machines. Neurocomputing 61:401–411, 2004.
Polat, K., and Güneş, S., Breast cancer diagnosis using least square support vector machine. Digital Signal Process. 17(4):694–701, 2007.
Mu, T., and Nandi, A. K., Breast cancer detection from FNA using SVM with different parameter tuning systems and SOM–RBF classifier. J. Franklin Inst. 344:285–311, 2007.
Polat, K., Güneş, S., and Arslan, A., A cascade learning system for classification of diabetes disease: Generalized discriminant analysis and least square support vector machine. Expert Syst. Appl. 34:482–487, 2008.
Wu, J., Diao, Y. B., Li, M. L., Fang, Y. P., and Ma, D. C., A semi-supervised learning based method: Laplacian support vector machine used in diabetes disease diagnosis. Interdiscip. Sci. Comput. Life Sci. 1:151–155, 2009.
Shawe-Taylor, J., and Cristianini, N., Kernel Methods for Pattern Analysis. Cambridge, UK, 2004.
Tan, Y., and Wang, J., A Support vector machine with a hybrid kernel and minimal Vapnik-Chervonenkis dimension. IEEE Trans. Knowl. Data Eng. 16:385–395, 2004.
Lessmann, S., Stahlbock, R., Crone, S. F., Genetic Algorithms for Support Vector Machine Model Selection, 2006 International Joint Conference on Neural Networks, Canada, 3063–3069, 2006.
Cai, Y. D., Liu, X. J., Xu, X., and Chou, K. C., Prediction of protein structural classes by support vector machines. Comput. Chem. 26:293–296, 2002.
Burbidge, R., Trotter, M., Buxton, B., and Holden, S., Drug design by machine learning: Support vector machines for pharmaceutiacal data analysis. Comput. Chem. 26:5–14, 2001.
Warmuth, M. K., Liao, J., Ratsch, G., Mathieson, M., Putta, S., and Lemmen, C., Active learning with support vector machines in the drug discovery process. J. Chem. Inf. Comput. Sci. 43:667–673, 2003.
Bao, L., and Sun, Z. R., Identifying genes related to drug anticancer mechanisms using support vector machine. FEBS Lett. 521:109–114, 2002.
Guler, N. F., and Kocer, S., Use of support vector machines and neural network in diagnosis of neuromuscular disorders. J. Medical Syst. 29(3):271–284, 2005.
El-Naqa, I., Yang, Y., Wernick, M., N., Galatsanos, N. P., Nishikawa, R., Support vector machine learning for detection of microcalcifications in mammograms, 2002 IEEE International Symposium on Biomedical Imaging, 201–204, 2002.
Nahar, J., Chen, Y.-P. P., Shawkat Ali, A. B. M., Microarray classification and rule based cancer Identification, International Conference on Information and Communication Technology, ICICT '07, 43–46, 2007.
Cortes, C., and Vapnik, V., Support vector networks. Machine Learning 20(3):273–297, 1995.
Bertsekas, D. P., Nonlinear Programming, Athena Scientific, 1995.
Jiang, Z. G., Fu, H. G., and Li, L. J., Support vector machine for mechanical faults classification. J. Zhejiang Univ. Sci. 6A(5):433–439, 2005.
Dash, P. K., Samantary, S. R., and Ganapati, P., Fault classification and section identification of an advanced series-compensated transmission line using support vector machine. IEEE Trans. Power Delivery 22:67–73, 2007.
Ben-Hur, A., and Weston, J., A User's guide to support vector machines, Data mining techniques for the life sciences. Meth. Mol. Biol. 609(2):223–239, 2010.
Melamud, E., and Moult, J., Evaluation of Disorder Predictions in CASP5. Proteins 53:561–565, 2003.
Yang, R. Z., Thomso, R., Mcneil, P., and Esnouf, R. M., RONN: The bio-basis function neural network technique applied to the detection of natively disordered regions in proteins. Bioinformatics 21:3369–3376, 2005.
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Ibrikci, T., Ustun, D. & Kaya, I.E. Diagnosis of Several Diseases by Using Combined Kernels with Support Vector Machine. J Med Syst 36, 1831–1840 (2012). https://doi.org/10.1007/s10916-010-9642-5
- Support Vector Machines
- Combined kernels
- Diagnosis diaseses