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Table 2 Comparison of overall accuracy on RNA-Seq gene expression datasets

From: BCDForest: a boosting cascade deep forest model towards the classification of cancer subtypes based on gene expression data

Code

Dataset

Sample

Gene

Class

Overall Accuracy

KNN

LR

RF

SVM

gcForest

BCDForest

1

PANCANCER

3594

8026

11

0.955

0.979

0.960

0.968

0.965

0.973

2

BRCA

514

3641

4

0.778

0.854

0.845

0.793

0.881

0.920

3

GBM

164

3180

4

0.694

0.651

0.702

0.619

0.741

0.806

4

LUNG

275

4000

3

0.710

0.744

0.791

0.786

0.830

0.867

5

COAD_I

264

3010

6

0.348

0.287

0.377

0.372

0.392

0.411

6

COAD_N

270

3006

3

0.699

0.631

0.696

0.700

0.711

0.730

7

COAD_T

282

3014

3

0.766

0.701

0.767

0.765

0.767

0.785

8

LIHC_I

347

4401

3

0.532

0.491

0.536

0.527

0.558

0.588

9

LIHC_N

400

4398

2

0.695

0.519

0.698

0.696

0.708

0.759

10

LIHC_T

347

4347

3

0.574

0.503

0.579

0.561

0.608

0.652