Research Article

A Novel Ensemble Credit Scoring Model Based on Extreme Learning Machine and Generalized Fuzzy Soft Sets

Table 5

Results for all single classifiers with AEnet-based feature selection.

Data setPerformance measurementSingle classifiers
C5.0SVM-RDBNBayesELM

GermanyAUC0.6900.7560.7620.7740.768
HM0.1810.2950.2480.2670.268
BS0.2300.1630.1720.1920.173
ACC0.7510.7590.7520.7580.765
AustraliaAUC0.8820.9020.9150.9120.883
HM0.6140.6350.6420.6230.624
BS0.1210.1040.1310.1210.130
ACC0.7490.7500.7550.7410.748
JapanAUC0.8610.9100.9190.9100.878
HM0.6080.6210.6310.6220.622
BS0.1160.1120.1260.1130.119
ACC0.7490.7520.7660.7530.758
IranAUC0.6310.6500.6120.7230.638
HM0.1370.1180.1080.2120.117
BS0.0680.0710.0780.0720.081
ACC0.6520.6450.6500.6520.658
Bene 1AUC0.7700.8100.8060.7730.798
HM0.3120.3450.3360.3020.320
BS0.2330.1770.1840.2640.241
ACC0.7210.7460.7190.7210.753
Bene 2AUC0.7630.8240.8150.7800.796
HM0.3540.3860.2690.2850.326
BS0.1320.1190.1370.1250.137
ACC0.7340.7340.7150.7230.747
ShuttleAUC0.6520.6820.6860.7230.729
HM0.3230.3370.3630.3210.362
BS0.2420.1550.1560.1880.165
ACC0.6850.6990.7430.7070.727
Skin_segmentAUC0.8320.7560.7960.8120.813
HM0.4950.5620.5580.5500.582
BS0.1410.1060.0980.1580.120
ACC0.6750.6920.7120.6960.702
MiniBooNEAUC0.8220.8450.8300.8440.841
HM0.6620.5660.5530.6330.589
BS0.1510.1130.1250.1660.135
ACC0.7070.7040.6890.7040.717
LC2017Q1AUC0.5910.5630.5570.6780.678
HM0.1070.0680.0560.1830.125
BS0.0670.0670.0710.0720.079
ACC0.6820.6870.6810.6960.719