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Table 6 Boosting Ensemble Classifiers training time, prediction time and error metrics result on GSE Dataset

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

ModelsNo. estimatorsMeanSTDRMSEMAER2PrecisionRecallAUCTrain timeTest time
DTBotc10.8630.0310.2740.0750.6730.9460.9250.9200.0520.005
SVMBotc10.6130.0370.6000.360− 0.5610.6400.6400.5000.3130.009
MLPBotc10.8290.0730.3560.1270.4500.8680.8730.8450.0460.003
DTBotc50.8590.0270.1660.0270.8810.9640.9730.9640.2490.015
SVMBotc50.6130.0370.6000.360− 0.5610.6400.6400.5001.4130.020
MLPBotc50.8260.0750.3750.1400.3900.8800.8600.8420.1300.025
DTBotc100.8730.0240.0000.0001.0001.0001.0001.0000.6700.041
SVMBotc100.6130.0370.6000.360− 0.5610.6400.6400.5001.3580.017
MLPBotc100.8570.0410.3210.1030.5540.8940.8970.8760.2980.023
DTBotc150.8730.0240.0000.0001.0001.0001.0001.0000.6700.041
SVMBotc150.6130.0370.6000.360− 0.5610.6400.6400.5001.3580.017
MLPBotc150.8570.0410.3210.1030.5540.8940.8970.8760.2980.023
DTBotc200.8910.0270.0000.0001.0001.0001.0001.0000.8100.079
SVMBotc200.6130.0370.6000.360− 0.5610.6400.6400.5001.3330.019
MLPBotc200.8530.0540.3360.1130.5090.8810.8870.8620.2620.008
DTBotc500.8910.0160.0000.0001.0001.0001.0001.0001.9180.153
SVMBotc500.6130.0370.6000.360− 0.5610.6400.6400.5001.3690.028
MLPBotc500.8510.0190.3510.1230.4650.8650.8770.8450.2240.013
DTBotc1000.8950.0200.0000.0001.0001.0001.0001.0005.1380.344
SVMBotc1000.6130.0370.6000.360− 0.5610.6400.6400.5001.6280.021
MLPBotc1000.8600.0350.3700.1370.4050.9110.8630.8600.3520.012
DTBotc1500.8940.0220.0000.0001.0001.0001.0001.0007.2220.614
SVMBotc1500.6130.0370.6000.360− 0.5610.6400.6400.5001.7240.022
MLPBotc1500.8420.0430.3560.1270.4500.8710.8730.8470.4350.014
DTBotc2000.8950.0260.0000.0001.0001.0001.0001.0009.3920.731
SVMBotc2000.6130.0370.6000.360− 0.5610.6400.6400.5001.6360.021
MLPBotc2000.8350.0280.3750.1400.3900.8760.8600.8400.3760.023