Abstract
Gene selection is one of the research issues for improving classification of microarray gene expression data. In this paper, a gene selection algorithm, which is based on the modified Recursive Feature Elimination (RFE) method, is integrated with a Support Vector Machine (SVM) to build a hybrid SVM-RFE model for cancer classification. The proposed model operates with a two-stage gene elimination scheme for finding a subset of expressed genes that indicate a disease. The effectiveness of the proposed model is evaluated using a multi-class lung cancer problem. The results show that the proposed SVM-RFE model is able to perform well with high classification accuracy rates.
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References
Lee, J.W., Lee, J.B., Park, M., Song, S.H.: An Extensive Comparison of Recent Classification Tools Applied to Microarray data. Computational Statistics & Data Analysis 48(4), 869–885 (2005)
Furey, T., Cristianini, N., Duffy, N., Bednarski, D., Schummer, M., Haussler, D.: Support Vector Machine Classification and Validation of Cancer Tissue Samples Using Microarray Expression Data. Bioinformatics 16(10), 906–914 (2000)
Luo, L., Ye, L., Luo, M., Huang, D., Peng, H., Yang, F.: Methods of Forward Feature Selection Based on the Aggregation of Classifiers Generated by Single Attribute. Computers in Biology and Medicine 41, 435–441 (2011)
Cai, R., Hao, Z., Yang, X., Wen, W.: An Efficient Gene Selection Algorithm Based on Mutual Information. Neurocomputing 72, 991–999 (2009)
Guyon, I., Weston, J., Barhill, S., Vapnik, V.: Gene Selection for Cancer Classification using Support Vector Machines. Machine Learning 46(1-3), 389–422 (2002)
Mundra, P.A., Rajapakse, J.C.: SVM-RFE with MRMR Filter for Gene Selection. IEEE Transactions on Nanobioscience 9(1), 31–37 (2010)
Yoon, S., Kim, S.: Mutual Information-Based SVM-RFE for Diagnostic Classification of Digitized Mammograms. Pattern Recognition Letters 30, 1489–1495 (2009)
Luo, L.-K., Huang, D.-F., Ye, L.-J., Zhou, Q.-F., Shao, G.-F., Peng, H.: Improving the Computational Efficiency of Recursive Cluster Elimination for Gene Selection. IEEE/ACM Transactions on Computational Biology and Bioinformatics 8(1), 122–129 (2011)
Zhou, X., Tuck, D.P.: MSVM-RFE: Extensions of SVM-RFE for Multiclass Gene Selection on DNA Microarray Data. Bioinformatics 23(9), 1106–1114 (2007)
Tang, Y., Zhang, Y.-Q., Huang, Z.: Development of Two-Stage SVM-RFE Gene Selection Strategy for Microarray Expression Data Analysis. IEEE/ACM Transactions on Computational Biology and Bioinformatics 4(3), 365–381 (2007)
Kreßel, U.H.-G.: Pairwise Classification and Support Vector Machines. In: Schölkopf, B., Burges, C.J.C., Smola, A.J. (eds.) Advances in Kernel Methods: Support Vector Learning, pp. 255–268. MIT Press, Cambridge (1999)
Knowledge Discovery and Data Mining in Biotechnology, http://www.uccor.edu.ar/paginas/seminarios/Software.htm
Bhattacharjee, A., Richards, W.G., Staunton, J., Li, C., Monti, S., Vasa, P., Ladd, C., Beheshti, J., Bueno, R., Gillette, M., Loda, M., Weber, G., Mark, E.J., Lander, E.S., Wong, W., Johnson, B.E., Golub, T.R., Sugarbaker, D.J., Meyerson, M.: Classification of Human Lung Carcinomas by Mrna Expression Profiling Reveals Distinct Adenocarcinoma Subclasses. Proc. Natl. Acad. Sci. U.S.A 98(24), 13790–13795 (2001)
LIBSVM: A library for Support Vector Machines, http://www.csie.ntu.edu.tw/~cjlin/libsvm
Kohavi, R.: A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. In: Mellish, C. (ed.) Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, vol. 2, pp. 1137–1143. Morgan Kaufmann, San Mateo (1995)
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Tan, P.L., Tan, S.C., Lim, C.P., Khor, S.E. (2011). A Modified Two-Stage SVM-RFE Model for Cancer Classification Using Microarray Data. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24955-6_79
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DOI: https://doi.org/10.1007/978-3-642-24955-6_79
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