Abstract
Classification of microarray data requires the selection of subsets of relevant genes in order to achieve good classification performance. This article presents a genetic embedded approach that performs the selection task for a SVM classifier. The main feature of the proposed approach concerns the highly specialized crossover and mutation operators that take into account gene ranking information provided by the SVM classifier. The effectiveness of our approach is assessed using three well-known benchmark data sets from the literature, showing highly competitive results.
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Alizadeh, A., Eisen, M.B., Davis, E., Ma, C., Lossos, I., Rosenwald, A., Boldrick, J., Sabet, H., Tran, T., Yu, X., Powell, J.I., Yang, L., Marti, G.E., Hudson Jr., J., Lu, L., Lewis, D.B., Tibshirani, R., Sherlock, G., Chan, W.C., Greiner, T.C., Weisenburger, D.D., Armitage, J.O., Warnke, R., Levy, R., Wilson, W., Grever, M.R., Byrd, J.C., Botstein, D., Brown, P.O., Staudt, L.M.: Distinct types of diffuse large B–cell lymphoma identified by gene expression profiling. Nature 403, 503–511 (2000)
Alon, U., Barkai, N., Notterman, D.A., Gish, K., Ybarra, S., Mack, D., Levine, A.J.: Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc. Natl. Acad. Sci. 96, 6745–6750 (1999)
Ambroise, C., McLachlan, G.J.: Selection bias in gene extraction on the basis of microarray gene-expression data. Proc. Natl. Acad. Sci. 99(10), 6562–6566 (2002)
Huerta, E.B., Duval, B., Hao, J.-K.: A hybrid ga/svm approach for gene selection and classification of microarray data. In: Rothlauf, F., Branke, J., Cagnoni, S., Costa, E., Cotta, C., Drechsler, R., Lutton, E., Machado, P., Moore, J.H., Romero, J., Smith, G.D., Squillero, G., Takagi, H. (eds.) EvoWorkshops 2006. LNCS, vol. 3907, pp. 34–44. Springer, Heidelberg (2006)
Boser, B.E., Guyon, I., Vapnik, V.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pp. 144–152. ACM Press, New York (1992)
Deb, K., Reddy, A.R.: Reliable classification of two-class cancer data using evolutionary algorithms. Biosystems 72(1-2), 111–129 (2003)
Dudoit, S., Fridlyand, J., Speed, T.P.: Comparison of discrimination methods for the classification of tumors using gene expression data. Journal of the American Statistical Association 97(457), 77–87 (2002)
Duda, R.O., Hart, P.E.: Pattern Classification and scene analysis. Wiley, Chichester (1973)
Golub, T.R., Slonim, D.K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J.P., Coller, H., Loh, M.L., Downing, J.R., Caligiuri, M.A., Bloomfield, C.D., Lander, E.S.: Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999)
Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Machine Learning 46(1-3), 389–422 (2002)
Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artificial Intelligence 97(1-2), 273–324 (1997)
Liu, J., Iba, H.: Selecting informative genes using a multiobjective evolutionary algorithm. In: Proceedings of the 2002 Congress on Evolutionary Computation, pp. 297–302. IEEE Computer Society Press, Los Alamitos (2002)
Marchiori, E., Jimenez, C.R., West-Nielsen, M., Heegaard, N.H.H.: Robust svm-based biomarker selection with noisy mass spectrometric proteomic data. In: Rothlauf, F., Branke, J., Cagnoni, S., Costa, E., Cotta, C., Drechsler, R., Lutton, E., Machado, P., Moore, J.H., Romero, J., Smith, G.D., Squillero, G., Takagi, H. (eds.) EvoWorkshops 2006. LNCS, vol. 3907, pp. 79–90. Springer, Heidelberg (2006)
Paul, T.K., Iba, H.: Selection of the most useful subset of genes for gene expression-based classification. In: Proceedings of the 2004 Congress on Evolutionary Computation, pp. 2076–2083. IEEE Computer Society Press, Los Alamitos (2004)
Peng, S., Xu, Q., Ling, X.B., Peng, X., Du, W., Chen, L.: Molecular classification of cancer types from microarray data using the combination of genetic algorithms and support vector machines. FEBS Letters 555(2), 358–362 (2003)
Rakotomamonjy, A.: Variable selection using svm-based criteria. Journal of Machine Learning Research 3, 1357–1370 (2003)
Weston, J., Elisseeff, A., Scholkopf, B., Tipping, M.: The use of zero-norm with linear models and kernel methods. Journal of Machine Learning Research 3(7-8), 1439–1461 (2003)
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Hernandez Hernandez, J.C., Duval, B., Hao, JK. (2007). A Genetic Embedded Approach for Gene Selection and Classification of Microarray Data. In: Marchiori, E., Moore, J.H., Rajapakse, J.C. (eds) Evolutionary Computation,Machine Learning and Data Mining in Bioinformatics. EvoBIO 2007. Lecture Notes in Computer Science, vol 4447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71783-6_9
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DOI: https://doi.org/10.1007/978-3-540-71783-6_9
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