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Table 4 Performance comparison results of testing on different datasets

From: Imbalanced biomedical data classification using self-adaptive multilayer ELM combined with dynamic GAN

Biomedical datasets Methods G-mean AUC F-measure
Breast cancer W-ELM 0.5679 0.8093 0.7658
SMOTE-ELM 0.7816 0.7981 0.7584
H-ELM 0.5835 0.6584 0.5967
PGM-ELM 0.9212 0.9013 0.9354
Liver patient W-ELM 0.7827 0.7439 0.9127
SMOTE-ELM 0.6379 0.5198 0.9218
H-ELM 0.5980 0.6919 0.7226
PGM-ELM 0.8016 0.8581 0.9304
Diabetic retinopathy W-ELM 0.6555 0.7849 0.9207
SMOTE-ELM 0.7554 0.7220 0.7404
H-ELM 0.6112 0.7493 0.6346
PGM-ELM 0.8778 0.8619 0.9715
Pima diabetes W-ELM 0.9360 0.9151 0.9724
SMOTE-ELM 0.6277 0.8792 0.5655
H-ELM 0.5041 0.8580 0.5000
PGM-ELM 0.9657 0.9324 0.9922