An Efficient Feature Selection Strategy Based on Multiple Support Vector Machine Technology with Gene Expression Data

BioMed Research International
Ying ZhangXianchun Zou

Abstract

The application of gene expression data to the diagnosis and classification of cancer has become a hot issue in the field of cancer classification. Gene expression data usually contains a large number of tumor-free data and has the characteristics of high dimensions. In order to select determinant genes related to breast cancer from the initial gene expression data, we propose a new feature selection method, namely, support vector machine based on recursive feature elimination and parameter optimization (SVM-RFE-PO). The grid search (GS) algorithm, the particle swarm optimization (PSO) algorithm, and the genetic algorithm (GA) are applied to search the optimal parameters in the feature selection process. Herein, the new feature selection method contains three kinds of algorithms: support vector machine based on recursive feature elimination and grid search (SVM-RFE-GS), support vector machine based on recursive feature elimination and particle swarm optimization (SVM-RFE-PSO), and support vector machine based on recursive feature elimination and genetic algorithm (SVM-RFE-GA). Then the selected optimal feature subsets are used to train the SVM classifier for cancer classification. We also use random forest feature selection (RF...Continue Reading

References

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Datasets Mentioned

BETA
GSE16443

Methods Mentioned

BETA
PCA
feature extraction
RNA-seq

Software Mentioned

RFE
edgeR
R
PSO
GA
GS
RFFS
SVM
- value ”

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