Table 4 Classification accuracy values of the proposed RBNRO-DE against popular ML classifiers

BLCA

1.0000

1.0000

0.9651

0.9884

0.9884

0.9419

0.9767

CESC

0.9839

1.0000

0.9839

0.9677

0.9839

0.9839

0.9839

CHOL

1.0000

1.0000

1.0000

1.0000

1.0000

1.0000

0.8889

COAD

1.0000

1.0000

0.9848

0.9848

0.9848

0.9848

0.9697

ESCA

0.9744

0.9915

0.9744

0.9744

0.9487

0.9744

0.9744

GBM

1.0000

1.0000

0.9688

1.0000

1.0000

1.0000

1.0000

HNSC

0.9938

1.0000

0.9558

0.8673

0.9469

0.9381

0.9469

KICH

1.0000

1.0000

1.0000

1.0000

1.0000

1.0000

1.0000

KIRC

0.9983

1.0000

0.9835

0.9752

0.8843

0.9256

0.9752

KIRP

1.0000

1.0000

1.0000

1.0000

1.0000

1.0000

1.0000

LIHC

1.0000

1.0000

1.0000

1.0000

0.9765

0.9647

0.9765

LUAD

0.9913

0.9922

0.9826

0.9826

0.9652

0.9913

0.9826

LUSC

1.0000

1.0000

0.9730

0.9820

0.8919

0.9730

0.9820

PAAD

1.0000

1.0000

1.0000

1.0000

1.0000

1.0000

1.0000

PCPG

1.0000

1.0000

1.0000

1.0000

1.0000

1.0000

1.0000

READ

1.0000

1.0000

1.0000

1.0000

1.0000

1.0000

1.0000

SARC

0.9811

0.9962

0.9811

0.9811

0.9811

0.9811

0.9811

SKCM

1.0000

1.0000

1.0000

1.0000

0.9524

1.0000

1.0000

STAD

1.0000

0.9900

0.9333

0.9333

0.8556

0.9111

0.9333

THCA

1.0000

1.0000

1.0000

1.0000

0.9643

1.0000

1.0000

THYM

1.0000

1.0000

1.0000

1.0000

1.0000

1.0000

1.0000

UCEC

0.9992

1.0000

0.9250

0.9250

0.9250

0.9500

0.9500

Ranking (\(\text {W|T|L}\))

0|15|7

\(\mathbf {7|14|1}\)

0|10|12

0|11|11

0|8|14

0|10|12

0|9|13