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Table 6 AUC values for multi-class data set (OVA)

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 0.75 0.77 0.76 0.84 0.75 0.85 0.86 0.87 0.86 0.79
Car 0.90 0.93 0.95 0.94 0.96 0.93 0.96 0.97 0.97 0.96 0.95
KEGG-U 0.87 0.92 0.93 0.93 0.94 0.92 0.94 0.94 0.96 0.94 0.93
Naïve Bayes Yeast 0.63 0.71 0.73 0.72 0.80 0.71 0.81 0.82 0.83 0.81 0.75
Car 0.87 0.90 0.91 0.90 0.93 0.90 0.93 0.93 0.94 0.93 0.92
KEGG-U 0.83 0.87 0.89 0.89 0.89 0.88 0.90 0.90 0.91 0.89 0.88
AdaBoostM1 Yeast 0.65 0.73 0.75 0.74 0.82 0.74 0.83 0.84 0.85 0.83 0.77
Car 0.89 0.92 0.93 0.93 0.95 0.92 0.95 0.96 0.96 0.95 0.94
KEGG-U 0.85 0.90 0.91 0.91 0.92 0.90 0.92 0.92 0.94 0.92 0.91
MultiLayer Perceptron Yeast 0.64 0.72 0.74 0.73 0.81 0.72 0.82 0.83 0.84 0.82 0.76
Car 0.88 0.91 0.92 0.91 0.94 0.91 0.94 0.94 0.95 0.94 0.93
KEGG-U 0.84 0.88 0.90 0.90 0.90 0.89 0.91 0.91 0.92 0.90 0.89
Overall average 0.79 0.84 0.86 0.85 0.89 0.84 0.89 0.9 0.91 0.89 0.86
  1. Italic values indicate highest result