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An embedded two-layer feature selection approach for microarray data analysis

Yang, Pengyi and Zhang, Zili 2009, An embedded two-layer feature selection approach for microarray data analysis, IEEE intelligent informatics bulletin, vol. 10, no. 1, pp. 24-32.

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Title An embedded two-layer feature selection approach for microarray data analysis
Author(s) Yang, Pengyi
Zhang, Zili
Journal name IEEE intelligent informatics bulletin
Volume number 10
Issue number 1
Start page 24
End page 32
Total pages 9
Publisher IEEE
Place of publication Los Alamitos, Calif.
Publication date 2009-12
ISSN 1727-5997
1727-6004
Keyword(s) feature selection
filter
wrapper
hybrid
microarrays
Summary Feature selection is an important technique in dealing with application problems with large number of variables and limited training samples, such as image processing, combinatorial chemistry, and microarray analysis. Commonly employed feature selection strategies can be divided into filter and wrapper. In this study, we propose an embedded two-layer feature selection approach to combining the advantages of filter and wrapper algorithms while avoiding their drawbacks. The hybrid algorithm, called GAEF (Genetic Algorithm with embedded filter), divides the feature selection process into two stages. In the first stage, Genetic Algorithm (GA) is employed to pre-select features while in the second stage a filter selector is used to further identify a small feature subset for accurate sample classification. Three benchmark microarray datasets are used to evaluate the proposed algorithm. The experimental results suggest that this embedded two-layer feature selection strategy is able to improve the stability of the selection results as well as the sample classification accuracy.
Notes This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
Language eng
Field of Research 080101 Adaptive Agents and Intelligent Robotics
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2009, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30029413

Document type: Journal Article
Collections: School of Information Technology
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