Tumor Classification Using Eigengene-Based Classifier Committee Learning Algorithm

Eigengene extracted by independent component analysis (ICA) is one kind of effective feature for tumor classification. In this letter, a novel tumor classification approach is proposed by using eigengene and support vector machine (SVM) based classifier committee learning (CCL) algorithm. In this method, a strategy of random feature subspace division is designed to improve the diversity of weaker classifiers. Gene expression data constructed by different feature subspaces are modeled by ICA, respectively. And the corresponding eigengene sets extracted by the ICA algorithm are used as the inputs of the weaker SVM classifiers. Moreover, a strategy of Bayesian sum rule (BSR) is designed to integrate the outputs of the weaker SVM classifiers, and used to provide a final decision for the tumor category. Experimental results on three DNA microarray datasets demonstrate that the proposed method is effective and feasible for tumor classification.

[1]  R. Tibshirani,et al.  Diagnosis of multiple cancer types by shrunken centroids of gene expression , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[2]  Kin-Man Lam,et al.  Depth Estimation of Face Images Based on the Constrained ICA Model , 2010, IEEE Transactions on Information Forensics and Security.

[3]  J. Mesirov,et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.

[4]  U. Alon,et al.  Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[5]  D. Ghosh.  Penalized Discriminant Methods for the Classification of Tumors from Gene Expression Data , 2003, Biometrics.

[6]  De-Shuang Huang,et al.  Independent component analysis-based penalized discriminant method for tumor classification using gene expression data , 2006, Bioinform..

[7]  Wolfram Liebermeister,et al.  Linear modes of gene expression determined by independent component analysis , 2002, Bioinform..

[8]  Xuelong Li,et al.  Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Jiri Matas,et al.  On Combining Classifiers , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[11]  Xing-Ming Zhao,et al.  Gene Expression Data Classification Using Consensus Independent Component Analysis , 2008, Genom. Proteom. Bioinform..

[12]  Bruno Torrésani,et al.  Blind Source Separation and the Analysis of Microarray Data , 2004, J. Comput. Biol..

[13]  L. Carin,et al.  Sequential modeling for identifying CpG island locations in human genome , 2002, IEEE Signal Processing Letters.

[14]  Johan A. K. Suykens,et al.  Systematic benchmarking of microarray data classification: assessing the role of non-linearity and dimensionality reduction , 2004, Bioinform..

[15]  Liang-Tien Chia,et al.  Scene classification using multiple features in a two-stage probabilistic classification framework , 2010, Neurocomputing.

[16]  Simon J. Godsill,et al.  Bayesian Image Modeling of cDNA Microarray Spots , 2007, IEEE Signal Processing Letters.

[17]  Xiao Liu,et al.  A Novel Representation Approach to DNA Sequence and Its Application , 2009, IEEE Signal Processing Letters.

[18]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[19]  Zhan-Li Sun.  An Extension of MISEP for Post–Nonlinear–Linear Mixture Separation , 2009, IEEE Transactions on Circuits and Systems II: Express Briefs.

[20]  S.K. Mitra,et al.  Studying DNA microarray data using independent component analysis , 2004, First International Symposium on Control, Communications and Signal Processing, 2004..

[21]  N. Iizuka,et al.  MECHANISMS OF DISEASE Mechanisms of disease , 2022 .