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Table 4 We report the performance of four classifiers in one experiment with best values per row shown in bold font

From: A new concordant partial AUC and partial c statistic for imbalanced data in the evaluation of machine learning algorithms

MeasuresLDALogRSVMNNNN-SVM
Whole Area
AUC82.9%77.1%84.8%86.0%1.2%
AUPRC+60.9%53.5%72.2%71.0%−1.2%
AUPRC−54.5%56.7%53.7%53.3%−0.4%
Partial Area i = 1
sPA75.0%69.2%78.8%79.2%0.4%
pAUC19.2%16.0%21.3%21.6%0.3%
pAUCc47.5%37.2%49.5%48.0%1.5%
Partial Area i = 2
sPA90.0%82.2%89.4%92.2%2.8%
pAUC29.7%27.1%29.5%30.4%0.9%
pAUCc18.5%22.9%17.4%21.0%3.6%
Partial Area i = 3
sPA100%100%99.7%100%0.3%
pAUC34.0%34.0%34.0%34.0%0%
pAUCc17.0%17.0%17.9%17.0%0.9%
sPA: sum of NN-SVM3.5%
pAUC: sum of NN-SVM1.2%
pAUCc: sum of NN-SVM1.2%