Table 1

Comparison of classification performance of SVMs and RFs without gene selection.

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


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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