Evaluation of Feature Selection Methods using Bagging and Boosting Ensemble Techniques on High Throughput Biological Data-Reference-Cited by-同舟云学术

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Evaluation of Feature Selection Methods using Bagging and Boosting Ensemble Techniques on High Throughput Biological Data

Published:2020-06-27 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2020 10th International Conference on Biomedical Engineering and Technology
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Author:

Wu Jiamin¹,Chen Shengjia²,Zhou Wenbin³,Wang Ningya⁴,Fan Ziling⁵

Affiliation:

1. Fuzhou University, Fuzhou, Fujian, China

2. Northeastern University, Shenyang, Liaoning, China

3. Columbia University in the City of New York, New York, NY, United States

4. Oklahoma State University, Stillwater, OK, United States

5. Georgetown University, Washington, DC, United States

Publisher

ACM

Reference17 articles.

1. Jiang H. Deng Y. Chen H. S. Tao L. Sha Q. Chen J. ... & Zhang S. (2004). Joint analysis of two microarray gene-expression data sets to select lung adenocarcinoma marker genes. BMC bioinformatics 5(1) 81. Jiang H. Deng Y. Chen H. S. Tao L. Sha Q. Chen J. ... & Zhang S. (2004). Joint analysis of two microarray gene-expression data sets to select lung adenocarcinoma marker genes. BMC bioinformatics 5(1) 81.

2. Robust biomarker identification for cancer diagnosis with ensemble feature selection methods

3. Dash M. & Liu H. (1997). Feature selection for classification. Intelligent data analysis 1(1-4) 131--156. Dash M. & Liu H. (1997). Feature selection for classification. Intelligent data analysis 1(1-4) 131--156.

4. Feature selection for ensembles;Opitz D. W.;AAAI/IAAI,1999

5. Zhou Z. H. (2015). Ensemble learning. Encyclopedia of biometrics 411--416. Zhou Z. H. (2015). Ensemble learning. Encyclopedia of biometrics 411--416.

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