Recursive feature selection with significant variables of support vectors.
Comput Math Methods Med
; 2012: 712542, 2012.
Article
en En
| MEDLINE
| ID: mdl-22927888
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.
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Análisis de Secuencia por Matrices de Oligonucleótidos
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Comput Math Methods Med
Asunto de la revista:
INFORMATICA MEDICA
Año:
2012
Tipo del documento:
Article
País de afiliación:
Taiwán