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Table 10 Cost-sensitive CoSen CNN results (accuracy) [19]

From: Survey on deep learning with class imbalance

Dataset Imbalance protocol SMOTE (%) RUS (%) SMOTE RSB (%) CoSen SVM (%) CoSen RF (%) SOSR CNN (%) Baseline CNN (%) CoSen CNN (%)
MNIST 10% of odd classes 94.5 92.1 96.0 96.8 96.3 97.8 97.6 98.6
CIFAR-100 10% of odd classes 32.2 28.8 37.5 39.9 39.0 55.8 55.0 60.1
Caltech-101 10% of odd classes 67.7 61.4 68.2 70.1 68.7 77.4 77.4 83.2
MIT-67 10% of odd classes 33.9 28.4 34.0 35.5 35.2 49.8 50.4 56.9
DIL Standard split 50.3 46.7 52.6 55.3 54.7 68.9 69.5 72.6
MLC Standard split 38.9 31.4 43.0 47.7 46.5 65.7 66.1 68.6
  1. Italic scores indicate the top performance for each data set