Abstract
Now the classification of different tumor types is of great importance in cancer diagnosis and drug discovery. It is more desirable to create an optimal ensemble for data analysis that deals with few samples and large features. In this paper, a new ensemble method for cancer data classification is proposed. The gene expression data is firstly preprocessed for normalization. Kernel Principal Component Analysis (KPCA) is then applied to extract features. Secondly, an intelligent approach is brought forward, which uses Support Vector Machine (SVM) as the base classifier and applied with Binary Particle Swarm Optimization (BPSO) for constructing ensemble classifiers. The leukemia and colon datasets are used for conducting all the experiments. Results show that the proposed method produces a good recognition rate comparing with some other advanced artificial techniques.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Sarkar, I., Planet, P., Bael, T., Stanley, S., Siddall, M., DeSalle, R.: Characteristic Attributes in Cancer Microarrays. Computers and Biomedical Research 35(2), 111–122 (2002)
Koller, D., Sahami, M.: Towards optimal feature selection. In: Machine Learning, Proceeding of 13th Int. Conf. (1996)
Azuaje, F.: A Computational Neural approach to Support the Discovery of Gene Function and Classes of Cancer. IEEE Transactions on Biomedical Engineering 48(3), 332–339 (2001)
Li, L., Weinberg, C., Darden, T., Pedersen, L.: Gene Selection for Sample Classification Based on Gene Expression Data: Study of Sensitivity to Choice of Parameters of the GA/KNN Method. Bioinformatics 17(12), 1131–1142 (2001)
Camp, N., Slattery, M.: Classification Tree Analysis: A Statistical Tool to Investigate Risk Factor Interactions with an Example for Colon Cancer. Cancer Causes Contr. 13(9), 813–823 (2002)
Chen, Y., Peng, L., Abraham, A.: Gene expression profiling using flexible neural trees. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds.) IDEAL 2006. LNCS, vol. 4224, pp. 1121–1128. Springer, Heidelberg (2006)
Tan, A., Gilbert, D.: Ensemble Machine Learning on Gene Expression Data for Cancer Classification. Applied Bioinformatics 2(3), 75–83 (2003)
Sergios, T., Konstantinos, K.: Pattern Recognition. China Machine Press (2002)
Scholkopf, B., Smola, A., Muller, K.R.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput. 10(5), 1299–1319 (1998)
Yang, J., Yang, J.Y., Frangi, A.F.: Combined Fisherfaces framework. Image and Vision Computing 21, 1037–1044 (2003)
Golub, G.H., Van Loan, C.F.: Matrix Computations. The Johns Hopkins University Press, Baltimore (1996)
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1999)
Kennedy, J., Eberhard, R.C.: Particle Swarm Optimization. In: Proceeding of IEEE International Conf. on Neural Networks, Piscataway, NJ, USA, pp. 1942–1948 (1995)
Zhou, Z.H., Wu, J., Tang, W.: Ensembling Neural Networks: Many Could Be Better Than All. Artificial Intelligence 137(1-2), 239–263 (2002)
Kennedy, J., Eberhart, R.C.: A Discrete Binary Version of the Particle Swarm Optimization. In: Proceeding Of the conference on Systems, Man, and Cybernetics SMC 1997, pp. 4104–4109 (1997)
Ben-Dor, A., Bruhn, L., Friedman, N., Nachman, I., Schummer, M., Yakhini, N.: Tissue classification with gene expression profiles. Computational Biology 7, 559–584 (2000)
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., Blomfield, C.D., Lander, E.S.: Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring. Science 286(12), 531–537 (1999)
Eisen, M.B., Brown, B.O.: DNA Arrays for Analysis of Gene Expression. Methods in Enzymology 303, 179–205 (1999)
Cho, S.B.: Exploring Features and Classifiers to Classify Gene Expression Profiles Of acute Leukemia. Artifical Intellegence 16(7), 1–13 (2002)
Harrington, C.A., Rosenow, C., Retief, J.: Monitoring Gene Expression Using DNA Microarrays. Curr. Opin. Microbiol. 3, 285–291 (2000)
Zhao, Y., Chen, Y., Zhang, X.: A novel ensemble approach for cancer data classification. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds.) ISNN 2007. LNCS, vol. 4492, pp. 1211–1220. Springer, Heidelberg (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhou, J., Pan, Y., Chen, Y., Liu, Y. (2009). Ensemble Classifiers Based on Kernel PCA for Cancer Data Classification. In: Huang, DS., Jo, KH., Lee, HH., Kang, HJ., Bevilacqua, V. (eds) Emerging Intelligent Computing Technology and Applications. With Aspects of Artificial Intelligence. ICIC 2009. Lecture Notes in Computer Science(), vol 5755. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04020-7_103
Download citation
DOI: https://doi.org/10.1007/978-3-642-04020-7_103
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04019-1
Online ISBN: 978-3-642-04020-7
eBook Packages: Computer ScienceComputer Science (R0)