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A multi-filter enhanced genetic ensemble system for gene selection and sample classification of microarray data

Yang, Pengyi, Zhou, Bing B., Zhang, Zili and Zomaya, Albert Y. 2010, A multi-filter enhanced genetic ensemble system for gene selection and sample classification of microarray data, BMC bioinformatics, vol. 11, no. Suppl - 1, pp. 1-12.

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Title A multi-filter enhanced genetic ensemble system for gene selection and sample classification of microarray data
Author(s) Yang, Pengyi
Zhou, Bing B.
Zhang, Zili
Zomaya, Albert Y.
Journal name BMC bioinformatics
Volume number 11
Issue number Suppl - 1
Start page 1
End page 12
Total pages 12
Publisher BioMed Central Ltd
Place of publication London, England
Publication date 2010
ISSN 1471-2105
Summary Background: Feature selection techniques are critical to the analysis of high dimensional datasets. This is especially true in gene selection from microarray data which are commonly with extremely high feature-to-sample ratio. In addition to the essential objectives such as to reduce data noise, to reduce data redundancy, to improve sample classification accuracy, and to improve model generalization property, feature selection also helps biologists to focus on the selected genes to further validate their biological hypotheses.
Results: In this paper we describe an improved hybrid system for gene selection. It is based on a recently proposed genetic ensemble (GE) system. To enhance the generalization property of the selected genes or gene subsets and to overcome the overfitting problem of the GE system, we devised a mapping strategy to fuse the goodness information of each gene provided by multiple filtering algorithms. This information is then used for initialization and mutation operation of the genetic ensemble system.
Conclusion: We used four benchmark microarray datasets (including both binary-class and multi-class classification problems) for concept proving and model evaluation. The experimental results indicate that the proposed multi-filter enhanced genetic ensemble (MF-GE) system is able to improve sample classification accuracy, generate more compact gene subset, and converge to the selection results more quickly. The MF-GE system is very flexible as various combinations of multiple filters and classifiers can be incorporated based on the data characteristics and the user preferences.
Notes This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Language eng
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970106 Expanding Knowledge in the Biological Sciences
HERDC Research category C1 Refereed article in a scholarly journal
HERDC collection year 2010
Copyright notice ©2010, The Authors
Persistent URL http://hdl.handle.net/10536/DRO/DU:30033725

Document type: Journal Article
Collections: School of Information Technology
Open Access Collection
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.