An Evaluation of Gene Selection Methods for Multi-class Microarray Data Classification

The fundamental power of microarrays lies in the ability to conduct parallel surveys of gene expression patterns for tens of thousands of genes across a wide range of cellular responses, phenotypes and conditions. Thus microarray data contain an overwhelming number of genes relative to the number of samples, presenting challenges for meaningful pattern discovery. This paper provides a comparative study of gene selection methods for multi-class classification of microarray data. We compare several feature ranking techniques, including new variants of correlation coefficients, and Support Vector Machine (SVM) method based on Recursive Feature Elimination (RFE). The results show that feature selection methods improve SVM classification accuracy in different kernel settings. The performance of feature selection techniques is problem-dependent. SVM-RFE shows an excellent performance in general, but often gives lower accuracy than correlation coefficients in low dimensions.

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