Classification of Brain Glioma by Using SVMs Bagging with Feature Selection
The degree of malignancy in brain glioma needs to be assessed by MRI findings and clinical data before operations. There have been previous attempts to solve this problem by using fuzzy max-min neural networks and support vector machines (SVMs), while in this paper, a novel algorithm named PRIFEB is proposed by combining bagging of SVMs with embedded feature selection for its individuals. PRIFEB is compared with the general case of bagging on UCI data sets, experimental results show PRIFEB can obtain better performance than the general case of bagging. Then, PRIFEB is used to predict the degree of malignancy in brain glioma, computation results show that PRIFEB obtains better accuracy than other several methods like bagging of SVMs and single SVMs does.
KeywordsSupport Vector Machine Feature Selection Prediction Accuracy Brain Glioma Embed Feature Selection
Unable to display preview. Download preview PDF.
- 4.Chow, L.K., Gobin, Y.P., Cloughesy, T.F., Sayre, J.W., Villablanca, J.P., Vinuela, F.: Prognostic Factors in Recurrent Glioblastoma Multiforme and Anaplastic Astrocytoma Treated with Selective Intra-Arteral Chemotherapy. AJNR Am. J. Neuroradiol 21, 471–478 (2000)Google Scholar
- 6.Li, G.Z., Yang, J., Ye, C.Z., Geng, D.: Degree Prediction of Malignancy in Brain Glioma Using Support Vector Machines. Computers in Biology and Medicine 36 (in press, 2006)Google Scholar
- 7.Dietterich, T.: Machine-Learning Research: Four Current Directions. The AI Magazine 18, 97–136 (1998)Google Scholar
- 12.Li, G.Z., Yang, J., Liu, G.P., Xue, L.: Feature Selection for Multi-Class Problems Using Support Vector Machines, Auckland, New Zealand. LNCS(LNAI), vol. 3173, pp. 292–300. Springer, Heidelberg (2004)Google Scholar
- 13.Arle, J.E., Morriss, C., Wang, Z., Zimmerman, R.A., Phillips, P.G., Sutton, L.N.: Prediction of Posterior Fossa Tumor Type in Children by Means of Magnetic Resonance Image Properties, Spectroscopy, and Neural Networks. Journal of Nonsurgical 86, 755–761 (1997)Google Scholar
- 14.Moody, J., Utans, J.: Principled Architecture Selection for Neural Networks: Application to Corporate Bond Rating Prediction. In: Moody, J.E., Hanson, S.J., Lippmann, R.P. (eds.) Advances in Neural Information Processing Systems, vol. 4, pp. 683–690. Morgan Kaufmann Publishers, Inc., San Francisco (1992)Google Scholar
- 16.Blake, C., Keogh, E., Merz, C.J.: UCI Repository of Machine Learning Databases. Technical report, Department of Information and Computer Science. University of California, Irvine, CA (1998), http://www.ics.uci.edu/~mlearn/MLRepository.htm