Recursive Feature Selection with Significant Variables of Support Vectors
Joint Authors
Chen, Chun-Houh
Huang, Chien-Hsun
Chang, Ching-Wei
Tsai, Chen-An
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-08-15
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
The development of DNA microarray makes researchers screen thousands of genes simultaneously and it also helps determine high- and low-expression level genes in normal and disease tissues.
Selecting relevant genes for cancer classification is an important issue.
Most of the gene selection methods use univariate ranking criteria and arbitrarily choose a threshold to choose genes.
However, the parameter setting may not be compatible to the selected classification algorithms.
In this paper, we propose a new gene selection method (SVM-t) based on the use of t-statistics embedded in support vector machine.
We compared the performance to two similar SVM-based methods: SVM recursive feature elimination (SVMRFE) and recursive support vector machine (RSVM).
The three methods were compared based on extensive simulation experiments and analyses of two published microarray datasets.
In the simulation experiments, we found that the proposed method is more robust in selecting informative genes than SVMRFE and RSVM and capable to attain good classification performance when the variations of informative and noninformative genes are different.
In the analysis of two microarray datasets, the proposed method yields better performance in identifying fewer genes with good prediction accuracy, compared to SVMRFE and RSVM.
American Psychological Association (APA)
Tsai, Chen-An& Huang, Chien-Hsun& Chang, Ching-Wei& Chen, Chun-Houh. 2012. Recursive Feature Selection with Significant Variables of Support Vectors. Computational and Mathematical Methods in Medicine،Vol. 2012, no. 2012, pp.1-12.
https://search.emarefa.net/detail/BIM-492541
Modern Language Association (MLA)
Tsai, Chen-An…[et al.]. Recursive Feature Selection with Significant Variables of Support Vectors. Computational and Mathematical Methods in Medicine No. 2012 (2012), pp.1-12.
https://search.emarefa.net/detail/BIM-492541
American Medical Association (AMA)
Tsai, Chen-An& Huang, Chien-Hsun& Chang, Ching-Wei& Chen, Chun-Houh. Recursive Feature Selection with Significant Variables of Support Vectors. Computational and Mathematical Methods in Medicine. 2012. Vol. 2012, no. 2012, pp.1-12.
https://search.emarefa.net/detail/BIM-492541
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-492541