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Table 10 Bagging ensemble classifiers training time, predicting time and error metrics result on GSE dataset

From: A comprehensive evaluation of ensemble learning for stock-market prediction

ModelsNo. estimatorsMeanSTDRMSEMAER2PrecisionRecallAUCTraining timeTesting time
DTBagc:10.8570.0350.2550.0650.7170.9380.9350.9240.0840.009
SVMBagc:10.8620.0390.3210.1030.5540.8790.8970.8680.1060.004
MLPBagc:10.9570.0360.2270.0510.7770.9570.9490.9432.0800.116
DTBagc:50.8810.0200.1660.0270.8810.9690.9730.9660.2020.024
SVMBagc:50.8590.0360.3460.1200.4800.8620.8800.8460.4120.016
MLPBagc:50.9720.0190.2030.0410.8220.9530.9590.94912.5050.652
DTBagc:100.8820.0160.1310.0170.9260.9790.9830.9780.4040.045
SVMBagc:100.8620.0340.3210.1030.5540.8760.8970.8650.8040.048
MLPBagc:100.9780.0200.1310.0170.9260.9790.9830.97822.6971.823
DTBagc:150.8780.0250.1170.0140.9410.9840.9860.9830.5370.050
SVMBagc:150.8630.0340.3310.1100.5240.8670.8900.8561.1320.043
MLPBagc:150.9840.0170.1170.0140.9410.9890.9860.98537.9232.578
DTBagc:200.8810.0200.0830.0070.9700.9950.9930.9930.7890.087
SVMBagc:200.8630.0350.3360.1130.5090.8670.8870.8531.8720.059
MLPBagc:200.9820.0180.1430.0210.9110.9790.9790.97655.5963.145
DTBagc:500.8970.0110.0000.0001.0001.0001.0001.0001.9270.139
SVMBagc:500.8630.0380.3360.1130.5090.8600.8870.8493.9130.143
MLPBagc:500.9710.0220.1170.0140.9410.9840.9860.983144.3677.525
DTBagc:1000.8930.0160.0000.0001.0001.0001.0001.0003.2670.240
SVMBagc:1000.8620.0350.3310.1100.5240.8600.8900.85210.1050.367
MLPBagc:1000.9760.0160.0830.0070.9700.9950.9930.993308.79616.173
DTBagc:1500.8940.0140.0000.0001.0001.0001.0001.0005.4750.378
SVMBagc:1500.8600.0340.3310.1100.5240.8640.8900.85415.3360.461
MLPBagc:1500.9750.0160.1010.0100.9550.9890.9900.988430.84723.734
DTBagc:2000.8880.0170.0000.0001.0001.0001.0001.0006.6410.495
SVMBagc:2000.8620.0370.3360.1130.5090.8630.8870.85118.5570.688
MLPBagc:2000.9690.0220.0830.0070.9700.9890.9930.990536.43334.095