Combined SNP feature selection based on relief and SVM-RFE
基于Relief和SVM-RFE的组合式SNP特征选择

The genome-wide association study (GWAS)on SNPs faces two big issues: high dimensional SNP data with small sample characteristics and complex mechanisms of genetic diseases. This paper proposed a combined SNP feature selection method through bring feature selection methods into GWAS. The method included two stages: filter stage, it used Relief algorithm to eliminate irrelevant SNP features, wrapper stage, it used support vector machine based recursive feature reduction (SVM-RFE) algorithm to select the key SNPs set. Experiments show that the proposed method has an obviously better performance than SVM RFE algorithm, and also gains higher classification accuracy than Relief-SVM algorithm, which provides an effective way for SNP genome-wide association analysis.