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Table 2 Average Kappa of different algorithms with different datasets

From: Adaptive swarm cluster-based dynamic multi-objective synthetic minority oversampling technique algorithm for tackling binary imbalanced datasets in biomedical data classification

Kappa

NN

SMOTE-NN

R-SMOTE-NN

SRA-NN

ASCB_DmSMOTE-NN

Thoraric Surgery

0.049

0.305

0.312 ± 0.48

0.670 ± 0.21

0.813 ± 0.11

Ecoli

0.502

0.807

0.723 ± 0.12

0.850 ± 0.05

0.848 ± 0.06

Sick Euthyroid

0.497

0.831

0.688 ± 0.13

0.824 ± 0.07

0.874 ± 0.05

Yeast_ML8

0.000

0.381

0.578 ± 0.23

0.968 ± 0.02

0.927 ± 0.04

Thyroid Sick

0.360

0.833

0.762 ± 0.12

0.906 ± 0.06

0.829 ± 0.11

Arrhythmia

0.068

0.761

0.826 ± 0.13

0.937 ± 0.04

0.966 ± 0.02

Mammography

0.436

0.794

0.729 ± 0.14

0.673 ± 0.16

0.932 ± 0.02

K_average

0.273

0.673

0.660 ± 0.22

0.833 ± 0.09

0.884 ± 0.06

  1. The italicized entries represent the best performance
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BioData Mining

ISSN: 1756-0381