Abstract
Computational Analysis of gene expression data is extremely difficult, due to the existence of a huge number of genes and less number of samples (limited number of patients). Thus,it is of significant importance to provide a subset of the most informative genesto a learning algorithm, for constructing robust prediction models. In this study, we propose a hybrid Intelligent Water Drop (IWD) - Support Vector Machines (SVM) algorithm, with weighted gene ranking as a heuristic, for simultaneous gene subset selection and cancer prediction. Our results, evaluated on three cancer datasets, demonstrate that the genes selected by the IWD technique yield classification accuracies comparable to previously reported algorithms.
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Sharma, S., Ghosh, S., Anantharaman, N., Jayaraman, V.K.: Simultaneous informative gene extraction and cancer classification using ACO-antMiner and ACO-random forests. In: Satapathy, S.C., Avadhani, P.S., Abraham, A. (eds.) Proceedings of the InConINDIA 2012. AISC, vol. 132, pp. 755–761. Springer, Heidelberg (2012)
Shah-Hosseini, H.: Problem solving by intelligent water drops. In: IEEE Congress on Evolutionary Computation, CEC 2007, pp. 3226–3231 (2007)
Han, J., Kamber, M.: Data mining: concepts and techniques. Morgan Kaufmann (2006)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. ACM SIGKDD Explorations Newsletter 11(1), 10–18 (2009)
Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, COLT 1992, pp. 144–152. ACM, New York (1992)
Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Machine Learning 46, 389–422 (2002)
Chang, C.-C., Lin, C.-J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1–27:27 (2011)
Kent ridge bio-medical dataset, http://datam.i2r.astar.edu.sg/datasets/krbd/
Martn-Merino, M., Blanco, A., De Las Rivas, J.: Combining dissimilarity based classifiers for cancer prediction using gene expression profiles. BMC Bioinformatics 8 (2008)
Cong, G., Tan, K.-L., Tung, A.K.H., Xu, X.: Mining top-k covering rule groups for gene expression data. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2005, pp. 670–681. ACM, New York (2005)
Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67–82 (1997)
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Kumar, M., Ghosh, S., Valadi, J., Siarry, P. (2013). Simultaneous Gene Selection and Cancer Classification Using a Hybrid Intelligent Water Drop Approach. In: Maji, P., Ghosh, A., Murty, M.N., Ghosh, K., Pal, S.K. (eds) Pattern Recognition and Machine Intelligence. PReMI 2013. Lecture Notes in Computer Science, vol 8251. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45062-4_90
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DOI: https://doi.org/10.1007/978-3-642-45062-4_90
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