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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