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Table 1 Comparison of classification performance of SVMs and RFs without 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 0.867 0.867 - 1
Dx-Ramaswamy2 AUC 0.821 0.767 SVM 0.409
Dx-Shipp AUC 0.992 0.973 SVM 0.500
Dx-Singh AUC 0.964 0.944 SVM 0.377
Px-Beer AUC 0.798 0.646 SVM 0.032
Px-Bhattacharjee AUC 0.519 0.561 RF 0.546
Px-Iizuka AUC 0.663 0.763 RF 0.061
Px-Pomeroy AUC 0.692 0.600 SVM 0.235
Px-Rosenwald AUC 0.689 0.629 SVM 0.140
Px-Veer AUC 0.747 0.754 RF 0.867
Px-Yeoh AUC 0.777 0.660 SVM 0.006
Dx-Alizadeh RCI 1.000 1.000 - 1
Dx-Armstrong RCI 0.944 0.894 SVM 0.658
Dx-Bhattacharjee RCI 0.895 0.763 SVM 0.015
Dx-Golub RCI 0.939 0.934 SVM 1
Dx-Khan RCI 1.000 1.000 - 1
Dx-Nutt RCI 0.775 0.733 SVM 0.498
Dx-Pomeroy RCI 0.823 0.611 SVM 0.031
Dx-Ramaswamy RCI 0.905 0.861 SVM 0.010
Dx-Staunton RCI 0.770 0.819 RF 0.249
Dx-Su RCI 0.958 0.910 SVM 0.004
Px-Veer2 RCI 0.451 0.304 SVM 0.004
  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.