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Table 3 Average Accuracy 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

Accuracy

NN

SMOTE-NN

R-SMOTE-NN

SRA-NN

ASCB_DmSMOTE-NN

ThoraricSurgery

0.848

0.653

0.686 ± 0.23

0.895 ± 0.05

0.902 ± 0.03

Ecoli

0.925

0.904

0.817 ± 0.18

0.959 ± 0.03

0.918 ± 0.02

Sick Euthyroid

0.936

0.916

0.781 ± 0.19

0.952 ± 0.03

0.927 ± 0.02

Yeast_ML8

0.926

0.690

0.756 ± 0.18

0.968 ± 0.04

0.959 ± 0.02

Thyroid Sick

0.953

0.916

0.852 ± 0.13

0.961 ± 0.03

0.946 ± 0.03

Arrhythmia

0.858

0.880

0.871 ± 0.11

0.958 ± 0.04

0.961 ± 0.03

Mammography

0.983

0.897

0.884 ± 0.10

0.960 ± 0.03

0.956 ± 0.02

A_average

0.919

0.837

0.807 ± 0.16

0.950 ± 0.03

0.938 ± 0.02

  1. The italicized entries represent the best performance