Gene selection using Gaussian kernel support vector machine based recursive feature elimination with adaptive kernel width strategy

Yong Mao; Xiaobo Zhou; Zheng Yin; Daoying Pi; Youxian Sun; Stephen T C Wong

doi:10.1007/11795131_116

Gene selection using Gaussian kernel support vector machine based recursive feature elimination with adaptive kernel width strategy

Yong Mao, Xiaobo Zhou, Zheng Yin, Daoying Pi, Youxian Sun, Stephen T C Wong

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

8 Scopus citations

Abstract

Recursive feature elimination based on non-linear kernel support vector machine (SVM-RFE) with parameter selection by genetic algorithm is an effective algorithm to perform gene selection and cancer classification in some degree, but its calculating complexity is too high for implementation. In this paper, we propose a new strategy to use adaptive kernel parameters in the recursive feature elimination algorithm implemented with Gaussian kernel SVMs as a better alternatives to the aforementioned algorithm for pragmatic reasons. The proposed method performs well in selecting genes and achieves high classification accuracies with these genes on two cancer datasets.

Original language	English (US)
Title of host publication	Rough Sets and Knowledge Technology - First International Conference, RSKT 2006, Proceedings
Publisher	Springer-Verlag
Pages	799-806
Number of pages	8
ISBN (Print)	3540362975, 9783540362975
DOIs	https://doi.org/10.1007/11795131_116
State	Published - 2006
Event	First International Conference on Rough Sets and Knowledge Technology, RSKT 2006 - Chongqing, China Duration: Jul 24 2006 → Jul 26 2006

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4062 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	First International Conference on Rough Sets and Knowledge Technology, RSKT 2006
Country/Territory	China
City	Chongqing
Period	7/24/06 → 7/26/06

Keywords

Feature selection
Machine learning
Recursive feature elimination
Support vector machine

ASJC Scopus subject areas

Computer Science(all)
Biochemistry, Genetics and Molecular Biology(all)
Theoretical Computer Science

Access to Document

10.1007/11795131_116

Cite this

Mao, Y., Zhou, X., Yin, Z., Pi, D., Sun, Y., & Wong, S. T. C. (2006). Gene selection using Gaussian kernel support vector machine based recursive feature elimination with adaptive kernel width strategy. In Rough Sets and Knowledge Technology - First International Conference, RSKT 2006, Proceedings (pp. 799-806). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4062 LNAI). Springer-Verlag. https://doi.org/10.1007/11795131_116

Gene selection using Gaussian kernel support vector machine based recursive feature elimination with adaptive kernel width strategy. / Mao, Yong; Zhou, Xiaobo; Yin, Zheng et al.

Rough Sets and Knowledge Technology - First International Conference, RSKT 2006, Proceedings. Springer-Verlag, 2006. p. 799-806 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4062 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Mao, Y, Zhou, X, Yin, Z, Pi, D, Sun, Y & Wong, STC 2006, Gene selection using Gaussian kernel support vector machine based recursive feature elimination with adaptive kernel width strategy. in Rough Sets and Knowledge Technology - First International Conference, RSKT 2006, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4062 LNAI, Springer-Verlag, pp. 799-806, First International Conference on Rough Sets and Knowledge Technology, RSKT 2006, Chongqing, China, 7/24/06. https://doi.org/10.1007/11795131_116

Mao Y, Zhou X, Yin Z, Pi D, Sun Y, Wong STC. Gene selection using Gaussian kernel support vector machine based recursive feature elimination with adaptive kernel width strategy. In Rough Sets and Knowledge Technology - First International Conference, RSKT 2006, Proceedings. Springer-Verlag. 2006. p. 799-806. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/11795131_116

Mao, Yong ; Zhou, Xiaobo ; Yin, Zheng et al. / Gene selection using Gaussian kernel support vector machine based recursive feature elimination with adaptive kernel width strategy. Rough Sets and Knowledge Technology - First International Conference, RSKT 2006, Proceedings. Springer-Verlag, 2006. pp. 799-806 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Recursive feature elimination based on non-linear kernel support vector machine (SVM-RFE) with parameter selection by genetic algorithm is an effective algorithm to perform gene selection and cancer classification in some degree, but its calculating complexity is too high for implementation. In this paper, we propose a new strategy to use adaptive kernel parameters in the recursive feature elimination algorithm implemented with Gaussian kernel SVMs as a better alternatives to the aforementioned algorithm for pragmatic reasons. The proposed method performs well in selecting genes and achieves high classification accuracies with these genes on two cancer datasets.",

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Gene selection using Gaussian kernel support vector machine based recursive feature elimination with adaptive kernel width strategy

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Keywords

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