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CLC number: Q789; R73

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Received: 2004-12-08

Revision Accepted: 2005-03-11

Crosschecked: 0000-00-00

Cited: 13

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Journal of Zhejiang University SCIENCE B 2005 Vol.6 No.10 P.961-973


Parameters selection in gene selection using Gaussian kernel support vector machines by genetic algorithm

Author(s):  MAO Yong, ZHOU Xiao-bo, PI Dao-ying, SUN You-xian, WONG Stephen T.C.

Affiliation(s):  National Laboratory of Industrial Control Technology, Institute of Modern Control Engineering, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   ymao@iipc.zju.edu.cn, zhou@crystal.harvard.edu, dypi@iipc.zju.edu.cn

Key Words:  Gene selection, Support vector machine (SVM), Recursive feature elimination (RFE), Genetic algorithm (GA), Parameter selection

MAO Yong, ZHOU Xiao-bo, PI Dao-ying, SUN You-xian, WONG Stephen T.C.. Parameters selection in gene selection using Gaussian kernel support vector machines by genetic algorithm[J]. Journal of Zhejiang University Science B, 2005, 6(10): 961-973.

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author="MAO Yong, ZHOU Xiao-bo, PI Dao-ying, SUN You-xian, WONG Stephen T.C.",
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publisher="Zhejiang University Press & Springer",

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%T Parameters selection in gene selection using Gaussian kernel support vector machines by genetic algorithm
%A MAO Yong
%A ZHOU Xiao-bo
%A PI Dao-ying
%A SUN You-xian
%A WONG Stephen T.C.
%J Journal of Zhejiang University SCIENCE B
%V 6
%N 10
%P 961-973
%@ 1673-1581
%D 2005
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2005.B0961

T1 - Parameters selection in gene selection using Gaussian kernel support vector machines by genetic algorithm
A1 - MAO Yong
A1 - ZHOU Xiao-bo
A1 - PI Dao-ying
A1 - SUN You-xian
A1 - WONG Stephen T.C.
J0 - Journal of Zhejiang University Science B
VL - 6
IS - 10
SP - 961
EP - 973
%@ 1673-1581
Y1 - 2005
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2005.B0961

In microarray-based cancer classification, gene selection is an important issue owing to the large number of variables and small number of samples as well as its non-linearity. It is difficult to get satisfying results by using conventional linear statistical methods. Recursive feature elimination based on support vector machine (SVM RFE) is an effective algorithm for gene selection and cancer classification, which are integrated into a consistent framework. In this paper, we propose a new method to select parameters of the aforementioned algorithm implemented with Gaussian kernel SVMs as better alternatives to the common practice of selecting the apparently best parameters by using a genetic algorithm to search for a couple of optimal parameter. Fast implementation issues for this method are also discussed for pragmatic reasons. The proposed method was tested on two representative hereditary breast cancer and acute leukaemia datasets. The experimental results indicate that the proposed method performs well in selecting genes and achieves high classification accuracies with these genes.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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