WERFE: A Gene Selection Algorithm Based on Recursive Feature Elimination and Ensemble Strategy
Gene selection algorithm in micro-array data classification problem finds a small set of genes which are most informative and distinctive. A well-performed gene selection algorithm should pick a set of genes that achieve high performance and the size of this gene set should be as small as possible....
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2020-05-01
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| Series: | Frontiers in Bioengineering and Biotechnology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fbioe.2020.00496/full |
| _version_ | 1818524065850195968 |
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| author | Qi Chen Qi Chen Zhaopeng Meng Zhaopeng Meng Ran Su Ran Su |
| author_facet | Qi Chen Qi Chen Zhaopeng Meng Zhaopeng Meng Ran Su Ran Su |
| author_sort | Qi Chen |
| collection | DOAJ |
| description | Gene selection algorithm in micro-array data classification problem finds a small set of genes which are most informative and distinctive. A well-performed gene selection algorithm should pick a set of genes that achieve high performance and the size of this gene set should be as small as possible. Many of the existing gene selection algorithms suffer from either low performance or large size. In this study, we propose a wrapper gene selection approach, named WERFE, within a recursive feature elimination (RFE) framework to make the classification more efficient. This WERFE employs an ensemble strategy, takes advantages of a variety of gene selection methods and assembles the top selected genes in each approach as the final gene subset. By integrating multiple gene selection algorithms, the optimal gene subset is determined through prioritizing the more important genes selected by each gene selection method and a more discriminative and compact gene subset can be selected. Experimental results show that the proposed method can achieve state-of-the-art performance. |
| first_indexed | 2024-12-11T05:52:36Z |
| format | Article |
| id | doaj.art-eb6d0d72a3064fb28637be10751d4020 |
| institution | Directory Open Access Journal |
| issn | 2296-4185 |
| language | English |
| last_indexed | 2024-12-11T05:52:36Z |
| publishDate | 2020-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Bioengineering and Biotechnology |
| spelling | doaj.art-eb6d0d72a3064fb28637be10751d40202022-12-22T01:18:46ZengFrontiers Media S.A.Frontiers in Bioengineering and Biotechnology2296-41852020-05-01810.3389/fbioe.2020.00496533484WERFE: A Gene Selection Algorithm Based on Recursive Feature Elimination and Ensemble StrategyQi Chen0Qi Chen1Zhaopeng Meng2Zhaopeng Meng3Ran Su4Ran Su5School of Computer Software, College of Intelligence and Computing, Tianjin University, Tianjin, ChinaMilitary Transportation Command Department, Army Military Transportation University, Tianjin, ChinaSchool of Computer Software, College of Intelligence and Computing, Tianjin University, Tianjin, ChinaTianjin University of Traditional Chinese Medicine, Tianjin, ChinaSchool of Computer Software, College of Intelligence and Computing, Tianjin University, Tianjin, ChinaFujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fuzhou, ChinaGene selection algorithm in micro-array data classification problem finds a small set of genes which are most informative and distinctive. A well-performed gene selection algorithm should pick a set of genes that achieve high performance and the size of this gene set should be as small as possible. Many of the existing gene selection algorithms suffer from either low performance or large size. In this study, we propose a wrapper gene selection approach, named WERFE, within a recursive feature elimination (RFE) framework to make the classification more efficient. This WERFE employs an ensemble strategy, takes advantages of a variety of gene selection methods and assembles the top selected genes in each approach as the final gene subset. By integrating multiple gene selection algorithms, the optimal gene subset is determined through prioritizing the more important genes selected by each gene selection method and a more discriminative and compact gene subset can be selected. Experimental results show that the proposed method can achieve state-of-the-art performance.https://www.frontiersin.org/article/10.3389/fbioe.2020.00496/fullWERFEgene selectionRFEensemblewrapper |
| spellingShingle | Qi Chen Qi Chen Zhaopeng Meng Zhaopeng Meng Ran Su Ran Su WERFE: A Gene Selection Algorithm Based on Recursive Feature Elimination and Ensemble Strategy Frontiers in Bioengineering and Biotechnology WERFE gene selection RFE ensemble wrapper |
| title | WERFE: A Gene Selection Algorithm Based on Recursive Feature Elimination and Ensemble Strategy |
| title_full | WERFE: A Gene Selection Algorithm Based on Recursive Feature Elimination and Ensemble Strategy |
| title_fullStr | WERFE: A Gene Selection Algorithm Based on Recursive Feature Elimination and Ensemble Strategy |
| title_full_unstemmed | WERFE: A Gene Selection Algorithm Based on Recursive Feature Elimination and Ensemble Strategy |
| title_short | WERFE: A Gene Selection Algorithm Based on Recursive Feature Elimination and Ensemble Strategy |
| title_sort | werfe a gene selection algorithm based on recursive feature elimination and ensemble strategy |
| topic | WERFE gene selection RFE ensemble wrapper |
| url | https://www.frontiersin.org/article/10.3389/fbioe.2020.00496/full |
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