Abstract
This paper proposes a Radial Basis Function Neural Network (RBFNN) which reproduces different Radial Basis Functions (RBFs) by means of a real parameter q, named q-Gaussian RBFNN. The architecture, weights and node topology are learnt through a Hybrid Algorithm (HA) with the iRprop + algorithm as the local improvement procedure. In order to test its overall performance, an experimental study with four gene microarray datasets with two classes taken from bioinformatic and biomedical domains is presented. The Fast Correlation–Based Filter (FCBF) was applied in order to identify salient expression genes from thousands of genes in microarray data that can directly contribute to determining the class membership of each pattern. After different gene subsets were obtained, the proposed methodology was performed using the selected gene subsets as the new input variables. The results confirm that the q-Gaussian RBFNN classifier leads to promising improvement on accuracy.
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References
Lancashire, L.J., Powe, D.G., Reis-Filho, J.S., Rakha, E., Lemetre, C., Weigelt, B., Abdel-Fatah, T.M., Green, A.R., Mukta, R., Blamey, R., Paish, E.C., Rees, R.C., Ellis, I.O., Ball, G.R.: A validated gene expression profile for detecting clinical outcome in breast cancer using artificial neural networks. Breast cancer research and treatment 120(1), 83–93 (2010)
Chu, F., Wang, L.: Applying rbf neural networks to cancer classification based on gene expressions. In: IEEE International Conference on Neural Networks - Conference Proceedings, pp. 1930–1934 (2006)
Verleysen, M., François, D., Simon, G., Wertz, V.: On the effects of dimensionality on data analysis with neural networks. In: Mira, J., Álvarez, J.R. (eds.) IWANN 2003. LNCS, vol. 2687, II105–II112. Springer, Heidelberg (2003)
Tsallis, C.: Possible generalization of boltzmann-gibbs statistics. Journal of Statistical Physics 52(1-2), 479–487 (1988)
Tsallis, C., Mendes, R.S., Plastino, A.R.: The role of constraints within generalized nonextensive statistics. Physica A: Statistical Mechanics and its Applications 261(3-4), 534–554 (1998)
Yu, L., Liu, H.: Efficient feature selection via analysis of relevance and redundancy. Journal of machine learning research 5, 1205–1224 (2004)
Martínez-Estudillo, A.C., Martínez-Estudilo, F.J., Hervás-Martínez, C., Garcá, N.: Evolutionary product unit based neural networks for regression. Neural Networks 19(4), 477–486 (2006)
Hervás-Martínez, C., Martínez-Estudillo, F.J., Carbonero-Ruz, M.: Multilogistic regression by means of evolutionary product-unit neural networks. Neural Networks 21(7), 951–961 (2008)
Igel, C., Hüsken, M.: Empirical evaluation of the improved rprop learning algorithms. Neurocomputing 50(6), 105–123 (2003)
Koller, D., Sahami, M.: Toward optimal feature selection. In: 13th Int. Conf. on Machine Learning, pp. 284–292 (1996)
Van’t Veer, L.J., Dai, H., Van de Vijver, M.J., He, Y.D., Hart, A.A.M., Mao, M., Peterse, H.L., Van Der Kooy, K., Marton, M.J., Witteveen, A.T., Schreiber, G.J., Kerkhoven, R.M., Roberts, C., Linsley, P.S., Bernards, R., Friend, S.H.: Gene expression profiling predicts clinical outcome of breast cancer. Nature 415(6871), 530–536 (2002)
Pomeroy, S.L., Tamayo, P., Gaasenbeek, M., Sturla, L.M., Angelo, M., McLaughlin, M.E., Kim, J.Y.H., Goumnerova, L.C., Black, P.M., Lau, C., Allen, J.C., Zagzag, D., Olson, J.M., Curran, T., Wetmore, C., Biegel, J.A., Poggio, T., Mukherjee, S., Rifkin, R., Califano, A., Stolovitzky, G., Louis, D.N., Mesirov, J.P., Lander, E.S., Golub, T.R.: Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature 415(6870), 436–442 (2002)
Alon, U., Barka, 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. Proceedings of the National Academy of Sciences of the United States of America 96(12), 6745–6750 (1999)
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(5439), 527–531 (1999)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, Data Management Systems, 2nd edn. Morgan Kaufmann, Elsevier (2005)
Sokolova, M., Lapalme, G.: A systematic analysis of performance measures for classification tasks. Information Processing and Management 45, 427–437 (2009)
Chang, C., Lin, C.: Libsvm: a library for support vector machines (2001)
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Fernández-Navarro, F., Hervás-Martínez, C., Gutiérrez, P.A., Ruiz, R., Riquelme, a.J.C. (2010). Evolutionary q-Gaussian Radial Basis Functions for Improving Prediction Accuracy of Gene Classification Using Feature Selection. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6352. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15819-3_43
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DOI: https://doi.org/10.1007/978-3-642-15819-3_43
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