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Table 11 AUC values for binary-class data set

From: Improved classification of large imbalanced data sets using rationalized technique: Updated Class Purity Maximization Over_Sampling Technique (UCPMOT)

Classifier

Data set

Over_sampling techniques

A

B

C

D

E

F

G

H

I

J

K

Random Forest

MiniBoone

0.86

0.90

0.92

0.92

0.93

0.91

0.94

0.94

0.96

0.95

0.92

Credit card

0.78

0.83

0.86

0.85

0.88

0.84

0.91

0.89

0.92

0.91

0.85

RLCP

0.40

0.48

0.50

0.49

0.77

0.48

0.79

0.79

0.81

0.80

0.51

Naïve Bayes

MiniBoone

0.85

0.87

0.90

0.91

0.91

0.90

0.92

0.92

0.93

0.92

0.90

credit card

0.76

0.80

0.82

0.82

0.87

0.81

0.89

0.87

0.90

0.90

0.83

RLCP

0.37

0.46

0.47

0.47

0.71

0.46

0.76

0.76

0.78

0.77

0.48

AdaBoostM1

MiniBoone

0.85

0.89

0.91

0.90

0.92

0.89

0.93

0.93

0.94

0.94

0.91

Credit card

0.77

0.81

0.84

0.83

0.89

0.82

0.90

0.88

0.91

0.90

0.85

RLCP

0.39

0.47

0.48

0.48

0.73

0.47

0.77

0.78

0.80

0.80

0.50

MultiLayer Perceptron

MiniBoone

0.85

0.88

0.91

0.90

0.91

0.89

0.92

0.92

0.93

0.93

0.91

Credit card

0.76

0.81

0.83

0.83

0.86

0.82

0.88

0.88

0.90

0.90

0.84

RLCP

0.38

0.47

0.47

0.47

0.73

0.47

0.77

0.77

0.79

0.79

0.49

Overall average

0.66

0.72

0.74

0.73

0.84

0.73

0.86

0.86

0.88

0.87

0.74

  1. Italic values indicate highest result