Comparison of Support Vector Machines to Other Classifiers Using Gene Expression Data

Abstract

ABSTRACT Support vector machines (SVMs) was shown to outperform Fisher's linear discriminant analysis and two classification trees (C4.5 and MOC1) in binary classification of microarray gene expression data (MGED) (Brown et al., 2000; Furey et al. 2000). However, multiclass classification is more commonly encountered in identifying tumor subtypes using MGED. Using MGED, Dudoit et al. (2002) showed that diagonal linear discriminant analysis (DLDA) outperformed other linear and quadratic discriminants, nearest neighbor, and classification trees with univariate splits. It is of interest, therefore, to compare performance of SVMs to DLDA and the latest two classification trees with linear splits, which performered better than trees with univariate splits, in multiclass classification of MGED. Furthermore, the performance of SVMs with different types of kernels were studied by three types of multiclass MGED. Finally, we investigate how irrelevant and correlated variables (features) influence the performance of the three classifiers. Some suggestions are made for multiclass classification of MGED.