Abstract
In this paper, kernel-based feature extraction method from gene expression data is proposed for cancer classification. The performances of four kernel algorithms, namely, kernel Fisher discriminant analysis (KFDA), kernel principal component analysis (KPCA), kernel partial least squares (KPLS), and kernel independent component analysis (KICA), are compared on three benchmarked datasets: breast cancer, leukemia and colon cancer. Experimental results show that the proposed kernel-based feature extraction methods work well for three benchmark gene dataset. Overall, the KPLS and KFDA show the best performance, and KPCA and KICA follow them.
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References
Kurita, T., Taguchi, T.: A Modification of Kernel-based Fisher Discriminant Analysis for Face Detection. In: Proceedings of International Conference on Automatic Face and Gesture Recognition, Washington DC, pp. 300–305 (2002)
Schölkopf, B., Smola, A., Müller, K.-R.: Kernel Principal Component Analysis. In: Schölkopf, B., Burges, C.J.C., Smola, A.J. (eds.) Advances in Kernel Methods - Support Vector Learning, pp. 327–352. MIT Press, Cambridge, MA (1999)
Müller, K., Mika, S., Rätsch, G., Tsuda, K., Schölkopf, B.: An Introduction to Kernel-Based Learning Algorithms. IEEE Trans. on Neural Networks, 180–201 (2001)
Rosipal, R., Trejo, L.J., Matthews, B.: Kernel PLS-SVC for Linear and Nonlinear Classification. In: Proceedings of the Twentieth International Conference on Machine Learning, Washington DC, pp. 640–647 (2003)
Bach, F.R., Jordan, M.I.: Kernel Independent Component Analysis. J. Machine Learning Research 3 (2002)
West, M., Blanchette, C., Dressman, H., Huang, E., Ishida, S., Spang, R., Zuzan, H., Marks, J.R., Nevins, J.R.: Predicting the Clinical Status of Human Breast Cancer Using Gene Expression Profiles. Proceedings of the National Academy of Science 98, 11462–11467 (2001)
Golub, T., Slonim, D., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J., Coller, H., Loh, M., Downing, J., Caligiuri, M., Bloomfield, C., Lander, E.: Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring. Science 28, 531–537 (1999)
Alon, U., Barkai, N., Notterman, D., Gish, K., Ybarra, S., Mack, D., Levine, A.: 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 Science 96, 6745–6750 (1999)
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Li, S., Liao, C. (2007). Feature Extraction for Cancer Classification Using Kernel-Based Methods. In: Li, K., Li, X., Irwin, G.W., He, G. (eds) Life System Modeling and Simulation. LSMS 2007. Lecture Notes in Computer Science(), vol 4689. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74771-0_19
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DOI: https://doi.org/10.1007/978-3-540-74771-0_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74770-3
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