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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