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Table 5 AUC values for multi-class data set (LVH)

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 Yeast 0.67 0.74 0.76 0.75 0.82 0.74 0.83 0.84 0.85 0.85 0.77
Car 0.90 0.93 0.94 0.94 0.94 0.93 0.95 0.96 0.96 0.96 0.93
KEGG-U 0.87 0.90 0.91 0.90 0.92 0.89 0.93 0.93 0.95 0.94 0.92
Naïve Bayes Yeast 0.62 0.68 0.71 0.70 0.79 0.69 0.80 0.79 0.81 0.80 0.76
Car 0.86 0.89 0.90 0.90 0.91 0.89 0.92 0.92 0.94 0.93 0.90
KEGG-U 0.82 0.85 0.86 0.86 0.88 0.85 0.89 0.89 0.90 0.88 0.87
AdaBoostM1 Yeast 0.65 0.72 0.75 0.74 0.81 0.73 0.82 0.82 0.83 0.82 0.76
Car 0.89 0.92 0.93 0.93 0.93 0.92 0.94 0.95 0.95 0.94 0.93
KEGG-U 0.85 0.88 0.89 0.89 0.91 0.88 0.91 0.91 0.92 0.90 0.90
MultiLayer Perceptron Yeast 0.64 0.70 0.73 0.72 0.80 0.71 0.81 0.81 0.82 0.81 0.78
Car 0.88 0.90 0.92 0.91 0.92 0.90 0.94 0.94 0.96 0.94 0.92
KEGG-U 0.84 0.87 0.88 0.88 0.90 0.87 0.91 0.90 0.91 0.89 0.88
Overall average 0.79 0.83 0.84 0.84 0.87 0.83 0.88 0.88 0.9 0.88 0.86
  1. Italic values indicate highest result