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Table 2 Comparison of classification performance of SVMs and RFs with gene selection.

From: A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification

Task & dataset Classification performance metric Classification performance Nominally superior method P-value
   SVM RF   
Dx-Alon AUC 0.938 0.917 SVM 0.626
Dx-Ramaswamy2 AUC 0.821 0.781 SVM 0.624
Dx-Shipp AUC 0.992 0.975 SVM 0.502
Dx-Singh AUC 0.964 0.972 RF 0.812
Px-Beer AUC 0.798 0.648 SVM 0.016
Px-Bhattacharjee AUC 0.519 0.561 RF 0.550
Px-Iizuka AUC 0.713 0.763 RF 0.750
Px-Pomeroy AUC 0.692 0.629 SVM 0.506
Px-Rosenwald AUC 0.689 0.631 SVM 0.128
Px-Veer AUC 0.758 0.754 SVM 0.954
Px-Yeoh AUC 0.777 0.716 SVM 0.082
Dx-Alizadeh RCI 1.000 1.000 - 1
Dx-Armstrong RCI 0.944 0.911 SVM 0.624
Dx-Bhattacharjee RCI 0.895 0.817 SVM 0.125
Dx-Golub RCI 0.953 0.934 SVM 1
Dx-Khan RCI 1.000 1.000 - 1
Dx-Nutt RCI 0.812 0.733 SVM 0.220
Dx-Pomeroy RCI 0.823 0.688 SVM 0.079
Dx-Ramaswamy RCI 0.911 0.880 SVM 0.066
Dx-Staunton RCI 0.876 0.856 SVM 0.626
Dx-Su RCI 0.958 0.922 SVM 0.078
Px-Veer2 RCI 0.451 0.371 SVM 0.262
  1. The performance is estimated using area under ROC curve (AUC) for binary classification tasks and relative classifier information (RCI) for multicategory tasks. See subsection "Statistical comparison among classifiers" for the description of statistical test employed to compute reported p-values. P-values shown with boldface denote statistically significant differences between classification methods at the 0.05 α level.