A multi-filter enhanced genetic ensemble system for gene selection and sample classification of microarray data
<p>Abstract</p> <p>Background</p> <p>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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2010-01-01
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| Series: | BMC Bioinformatics |
| _version_ | 1819028705811365888 |
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| author | Zhang Zili Zhou Bing B Yang Pengyi Zomaya Albert Y |
| author_facet | Zhang Zili Zhou Bing B Yang Pengyi Zomaya Albert Y |
| author_sort | Zhang Zili |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>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.</p> |
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| format | Article |
| id | doaj.art-cdde91dfa0ed4f928805c14e1e392867 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-21T06:02:37Z |
| publishDate | 2010-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-cdde91dfa0ed4f928805c14e1e3928672022-12-21T19:13:44ZengBMCBMC Bioinformatics1471-21052010-01-0111Suppl 1S510.1186/1471-2105-11-S1-S5A multi-filter enhanced genetic ensemble system for gene selection and sample classification of microarray dataZhang ZiliZhou Bing BYang PengyiZomaya Albert Y<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>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.</p> |
| spellingShingle | Zhang Zili Zhou Bing B Yang Pengyi Zomaya Albert Y A multi-filter enhanced genetic ensemble system for gene selection and sample classification of microarray data BMC Bioinformatics |
| title | A multi-filter enhanced genetic ensemble system for gene selection and sample classification of microarray data |
| title_full | A multi-filter enhanced genetic ensemble system for gene selection and sample classification of microarray data |
| title_fullStr | A multi-filter enhanced genetic ensemble system for gene selection and sample classification of microarray data |
| title_full_unstemmed | A multi-filter enhanced genetic ensemble system for gene selection and sample classification of microarray data |
| title_short | A multi-filter enhanced genetic ensemble system for gene selection and sample classification of microarray data |
| title_sort | multi filter enhanced genetic ensemble system for gene selection and sample classification of microarray data |
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