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Journal of Big Data

Table 6 Precision values of the proposed RBNRO-DE against popular ML classifiers

From: Gene selection via improved nuclear reaction optimization algorithm for cancer classification in high-dimensional data

Benchmark

RBNRO-DE with k-NN

RBNRO-DE with SVM

k-NN

SVM

DT

RF

XGBoost

BLCA

1.0000

1.0000

0.9634

0.9875

0.9875

0.9405

0.9753

CESC

0.9839

1.0000

0.9839

0.9836

0.9839

0.9839

0.9839

CHOL

1.0000

1.0000

1.0000

1.0000

1.0000

1.0000

1.0000

COAD

1.0000

1.0000

0.9828

0.9828

0.9828

0.9828

0.9661

ESCA

0.9737

0.9912

0.9737

0.9737

0.9487

0.9737

0.9737

GBM

1.0000

1.0000

1.0000

1.0000

1.0000

1.0000

1.0000

HNSC

0.9929

1.0000

0.9510

0.8661

0.9596

0.9327

0.9505

KICH

1.0000

1.0000

1.0000

1.0000

1.0000

1.0000

1.0000

KIRC

1.0000

1.0000

1.0000

0.9903

0.8879

0.9204

0.9810

KIRP

1.0000

1.0000

1.0000

1.0000

1.0000

1.0000

1.0000

LIHC

1.0000

1.0000

1.0000

1.0000

0.9733

0.9605

0.9733

LUAD

1.0000

1.0000

1.0000

1.0000

0.9899

1.0000

1.0000

LUSC

1.0000

1.0000

0.9895

1.0000

0.9468

0.9794

0.9896

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

1.0000

1.0000

1.0000

STAD

1.0000

0.9888

0.9294

0.9294

0.9024

0.9277

0.9294

THCA

1.0000

1.0000

1.0000

1.0000

0.9800

1.0000

1.0000

THYM

1.0000

1.0000

1.0000

1.0000

1.0000

1.0000

1.0000

UCEC

1.0000

1.0000

1.0000

1.0000

1.0000

1.0000

1.0000

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

1|17|4

\(\mathbf {4|17|1}\)

0|14|8

0|14|8

0|10|12

0|12|10

0|12|10

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