Table 2

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

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


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.

Statnikov et al. BMC Bioinformatics 2008 9:319   doi:10.1186/1471-2105-9-319

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