Feature selection in independent component subspace for microarray data classification

Chun-Hou Zheng

doi:10.1016/j.neucom.2006.02.006

2006-10-01

PRE

郑春厚 (Chun-Hou Zheng)

Li Shang

DOI：10.1016/j.neucom.2006.02.006

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摘要

En 中文

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.

Keyword:

independent component analysis (ICA)

feature selection

support vector machines (SVM)

gene expression data

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期刊

Neurocomputing

IF:

6.5

论文数:

2.5W

被引数:

6.5W

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