Recursive feature selection with significant variables of support vectors
type: Item
instance of: scholarly article
title: Recursive feature selection with significant variables of support vectors
zbMATH Open document ID: 1401.92146
DOI: 10.1155/2012/712542
author: Chen-An Tsai
author: Chien-Hsun Huang
author: Ching-Wei Chang
author: Chun-Houh Chen
published in: Computational \& Mathematical Methods in Medicine
publication date: 2012-10-10T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 92D10
Mathematics Subject Classification ID: 92C50
Mathematics Subject Classification ID: 62P10
Mathematics Subject Classification ID: 68T05
zbMATH DE Number: 6092384
zbMATH Keywords: cancer classification
zbMATH Keywords: genge selection
zbMATH Keywords: t-statistics
zbMATH Keywords: support vector machine
zbMATH Keywords: recursive support vector machine
Wikidata QID: Q36184659
describes a project that uses: GeneSrF
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2012/712542
OpenAlex ID: W1969716378
cites work: Q3093222
cites work: An introduction to support vector machines and other kernel-based learning methods.
cites work: Gene selection for cancer classification using support vector machines
description: scientific article; zbMATH DE number 6092384