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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