Recursive feature selection with significant variables of support vectors

4Citations
Citations of this article
46Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

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. © 2012 Chen-An Tsai et al.

Cite

CITATION STYLE

APA

Tsai, C. A., Huang, C. H., Chang, C. W., & Chen, C. H. (2012). Recursive feature selection with significant variables of support vectors. Computational and Mathematical Methods in Medicine, 2012. https://doi.org/10.1155/2012/712542

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free