4.6 Article

Feature selection in independent component subspace for microarray data classification

期刊

NEUROCOMPUTING
卷 69, 期 16-18, 页码 2407-2410

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ELSEVIER
DOI: 10.1016/j.neucom.2006.02.006

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independent component analysis (ICA); feature selection; support vector machines (SVM); gene expression data

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A novel method for microarray data classification is proposed in this letter. In this scheme, the sequential floating forward selection (SFFS) technique is used to select the independent components of the DNA microarray data for classification. Experimental results show that the method is efficient and feasible. (c) 2006 Elsevier B.V. All rights reserved.

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