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A Comparison of SVM-based Criteria in Evolutionary Method for Gene Selection and Classification of Microarray Data

Authors: Rameswar Debnath, Haruhisa Takahashi


An evolutionary method whose selection and recombination operations are based on generalization error-bounds of support vector machine (SVM) can select a subset of potentially informative genes for SVM classifier very efficiently [7]. In this paper, we will use the derivative of error-bound (first-order criteria) to select and recombine gene features in the evolutionary process, and compare the performance of the derivative of error-bound with the error-bound itself (zero-order) in the evolutionary process. We also investigate several error-bounds and their derivatives to compare the performance, and find the best criteria for gene selection and classification. We use 7 cancer-related human gene expression datasets to evaluate the performance of the zero-order and first-order criteria of error-bounds. Though both criteria have the same strategy in theoretically, experimental results demonstrate the best criterion for microarray gene expression data.

Keywords: support vector machine, generalization error-bound, feature selection, evolutionary algorithm, microarray data

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[1] T. Jirapech-Umpai and S. Aitken, "Feature selection and classification for microarray data analysis: Evolutionary methods for identifying predictive genes", BMC Bioinformatics, vol. 6, no. 148, 2005.
[2] I. Guyon, J. Weston, S. Barnhill and V. Vapnik, "Gene selection for cancer classification using support vector machines", Machine Learning, vol. 46, pp. 389-422, 2002.
[3] J. Weston, S. Mukherjee, O. Chapelle, M. Pontil, T. Poggio and V. Vapnik, "Feature selection for svms", Advanced in Neural Information Processing Systems 13, 2001.
[4] H.-L. Huang and F. -L. Chang, "ESVM: Evolutionary support vector machine for automatic feature selection and classification of microarray data", Bio Systems, vol. 90, pp. 516-528, 2007.
[5] A. Rakotomamonjy, "Variable selection using SVM-based criteria", Journal of Machine Learning Research, vol. 3, pp. 1357-1370, 2003.
[6] A. Statnikov, C. F. Aliferis, I. Tsamardinos, D. Hardin and S. Levy, "A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis", Bioinformatics, vol. 21, no. 5, pp. 631-643, 2005.
[7] R. Debnath and T. Kurita, "An evolutionary approach for gene selection and classification of microarray data based on SVM error-bound theories", BioSyatems, vol. 100, issue 1, pp. 39-46, 2010.
[8] M. Opper and O. Winther, "Gaussian process and SVM: Mean field and leave-one-out", Smola, A., Bartlett, P., Sch¨olkopf, B., Schuurmans, D. (Eds.), Advances in large margin classifiers, Cambridge, MA:MIT Press, pp. 311-326, 2000.
[9] T.S. Jaakkola and D. Haussler, "Probabilistic kernel regression models", in Proc. 1999 Conference on AI and Statistics, Floria, USA, 1999.
[10] X. Zhou, and D. P. Tuck, "Gene selection using a new error bound for support vector machines", in Proc. Eleventh Annual International Conference on Research in Computational Molecular Biology, San Francisco, USA, 2007.
[11] O. Chapelle, V. Vapnik, O. Bousquet, S. Mukherjee, "Choosing multiple parameters for support vector machines", Machine Learning, vol. 46, pp. 131-159, 2002.
[12] V. Vapnik, Statistical Learning Theory, New York:Wiley, 1998.