Abstract
This paper presents a tumor classification approach by using eigengene and support vector machine (SVM) based classifier committee learning (CCL) algorithm. In this method, first, multiple sample feature subspaces of gene expression data are extracted by random subspace method. Then, the gene expression data constructed by these subspaces are modeled by independent component analysis (ICA), respectively. And the corresponding eigengene sets are extracted by the ICA algorithm. Finally, Bayesian sum rule (BSR) based SVM CCL algorithm is applied on these feature sets and the unknown labels are predicted. Experimental results on two DNA microarray datasets demonstrate that the proposed method is efficient and feasible for the tumor classification.
This work was supported by a grant from the National Science Foundation of China (60905023).
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© 2012 Springer-Verlag Berlin Heidelberg
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Sun, ZL., Sun, TT., Liu, Y. (2012). An Eigengene-Based Classifier Committee Learning Algorithm for Tumor Classification. In: Huang, DS., Ma, J., Jo, KH., Gromiha, M.M. (eds) Intelligent Computing Theories and Applications. ICIC 2012. Lecture Notes in Computer Science(), vol 7390. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31576-3_12
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DOI: https://doi.org/10.1007/978-3-642-31576-3_12
Publisher Name: Springer, Berlin, Heidelberg
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