Table 9 Comparison of AUC values for testing performance of KNN models trained on training sets processed by various sampling algorithms.

0.7316 ± 0.0001

0.7326 ± 0.0001

0.7286 ± 0.0001

0.7290 ± 0.0001

0.7319 ± 0.0001

0.7287 ± 0.0001

0.7390 ± 0.0001

0.7393 ± 0.0001

0.7858 ± 0.0179

0.7717 ± 0.0215

0.7433 ± 0.0235

0.7533 ± 0.0239

0.7567 ± 0.0210

0.7783 ± 0.0176

0.7750 ± 0.0204

0.7892 ± 0.0187

0.8918 ± 0.0000

0.9061 ± 0.0000

0.9096 ± 0.0000

0.9075 ± 0.0000

0.8986 ± 0.0000

0.9059 ± 0.0000

0.8930 ± 0.0000

0.9007 ± 0.0000

0.9004 ± 0.0165

0.9062 ± 0.0121

0.8983 ± 0.0125

0.9115 ± 0.0162

0.9080 ± 0.0119

0.8744 ± 0.0118

0.9269 ± 0.0196

0.9207 ± 0.0115

0.8444 ± 0.0000

0.8594 ± 0.0122

0.8569 ± 0.0125

0.8994 ± 0.0100

0.8464 ± 0.0115

0.8433 ± 0.0122

0.8161 ± 0.0100

0.9019 ± 0.0115

0.7013 ± 0.0007

0.7107 ± 0.0012

0.7177 ± 0.0014

0.6994 ± 0.0010

0.6884 ± 0.0011

0.6922 ± 0.0010

0.6477 ± 0.0011

0.7200 ± 0.0007

0.7295 ± 0.0016

0.7843 ± 0.0013

0.7792 ± 0.0015

0.7691 ± 0.0016

0.7920 ± 0.0012

0.7859 ± 0.0013

0.6520 ± 0.0013

0.7999 ± 0.0011

0.5372 ± 0.0000

0.5984 ± 0.0000

0.5971 ± 0.0000

0.6005 ± 0.0000

0.5908 ± 0.0000

0.5862 ± 0.0000

0.5157 ± 0.0000

0.6061 ± 0.0000

0.5685 ± 0.0069

0.5910 ± 0.0050

0.5868 ± 0.0053

0.5619 ± 0.0045

0.5886 ± 0.0039

0.6087 ± 0.0045

0.5171 ± 0.0038

0.6266 ± 0.0069

0.7287 ± 0.0021

0.7379 ± 0.0013

0.7260 ± 0.0027

0.7437 ± 0.0033

0.7451 ± 0.0023

0.7450 ± 0.0022

0.6768 ± 0.0017

0.7623 ± 0.0029

0.6054 ± 0.0000

0.6098 ± 0.0000

0.6109 ± 0.0000

0.6076 ± 0.0000

0.6565 ± 0.0000

0.6500 ± 0.0000

0.6087 ± 0.0000

0.6424 ± 0.0000

0.6769 ± 0.0031

0.7054 ± 0.0032

0.7019 ± 0.0011

0.6940 ± 0.0027

0.7035 ± 0.0034

0.7065 ± 0.0031

0.5912 ± 0.0023

0.6902 ± 0.0042

0.5806 ± 0.0112

0.5746 ± 0.0195

0.5593 ± 0.0158

0.6112 ± 0.0158

0.5794 ± 0.0175

0.5801 ± 0.0208

0.5264 ± 0.0075

0.5814 ± 0.0187