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A novel forward gene selection algorithm for microarray data
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DOI：10.1016/j.neucom.2013.12.012.png)
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This paper investigates the gene selection problem for microarray data with small samples and variant correlation. Most existing algorithms usually require expensive computational effort, especially under thousands of gene conditions. The main objective of this paper is to effectively select the most informative genes from microarray data, while making the computational expenses affordable. This is achieved by proposing a novel forward gene selection algorithm (FGSA). To overcome the small samples' problem, the augmented data technique is firstly employed to produce an augmented data set. Taking inspiration from other gene selection methods, the L-2-norm penalty is then introduced into the recently proposed fast regression algorithm to achieve the group selection ability. Finally, by defining a proper regression context, the proposed method can be fast implemented in the software, which significantly reduces computational burden. Both computational complexity analysis and simulation results confirm the effectiveness of the proposed algorithm in comparison with other approaches. (C) 2014 Elsevier B.V. All rights reserved.
Keyword:
Computational complexity analysis
Data augmentation
Fast regression algorithm
Gene selection
Small samples
Variant correlation
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
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