Combining support vector machines and the t-statistic for gene selection in DNA microarray data analysis

Yang, T; Kecman, V; Cao, L; Zhang, C

Combining support vector machines and the t-statistic for gene selection in DNA microarray data analysis

Yang, T Kecman, V Cao, L

Zhang, C

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010, 6119 LNAI (PART 2), pp. 55 - 62
Issue Date:: 2010-12-01

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Field	Value	Language
dc.contributor.author	Yang, T	en_US
dc.contributor.author	Kecman, V	en_US
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.date.issued	2010-12-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010, 6119 LNAI (PART 2), pp. 55 - 62	en_US
dc.identifier.isbn	3642136710	en_US
dc.identifier.isbn	9783642136719	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16094
dc.description.abstract	This paper proposes a new gene selection (or feature selection) method for DNA microarray data analysis. In the method, the t-statistic and support vector machines are combined efficiently. The resulting gene selection method uses both the data intrinsic information and learning algorithm performance to measure the relevance of a gene in a DNA microarray. We explain why and how the proposed method works well. The experimental results on two benchmarking microarray data sets show that the proposed method is competitive with previous methods. The proposed method can also be used for other feature selection problems. © 2010 Springer-Verlag Berlin Heidelberg.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-642-13672-6_6	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Combining support vector machines and the t-statistic for gene selection in DNA microarray data analysis	en_US
dc.type	Conference Proceeding
utslib.citation.volume	PART 2	en_US
utslib.citation.volume	6119 LNAI	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Hyderabad, India	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (International)
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Strength - ACRI - Australia China Relations Institute
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	PART 2	en_US
pubs.publication-status	Published	en_US
pubs.volume	6119 LNAI	en_US

Abstract:

This paper proposes a new gene selection (or feature selection) method for DNA microarray data analysis. In the method, the t-statistic and support vector machines are combined efficiently. The resulting gene selection method uses both the data intrinsic information and learning algorithm performance to measure the relevance of a gene in a DNA microarray. We explain why and how the proposed method works well. The experimental results on two benchmarking microarray data sets show that the proposed method is competitive with previous methods. The proposed method can also be used for other feature selection problems. © 2010 Springer-Verlag Berlin Heidelberg.

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http://hdl.handle.net/10453/16094