Table 2: The standard deviation of the 34 algorithms.

AlgorithmStandard deviation
Basic training datasetIndependent test dataset
ACC (%)MCC (%)ACC (%)MCC (%)

AdaBoostM10.611.161.001.94
J480.881.761.422.84
IBk0.521.011.182.21
MultiClassClassifier0.601.211.042.09
PART0.551.251.262.54
MultilayerPerceptron1.262.522.223.04
KStar0.721.411.072.00
Bagging0.761.510.430.88
NBTree0.821.642.044.09
Decorate0.731.471.162.25
RandomForest0.671.320.621.25
JRip0.480.962.254.43
RandomCommittee0.510.991.232.59
FilteredClassifier1.112.221.162.32
ClassificationViaRegression0.961.910.801.57
Dagging0.701.381.002.00
AttributeSelectedClassifier0.851.710.661.40
REPTree0.711.461.322.66
SMO0.551.101.062.11
J48graft1.062.121.402.81
Ridor1.012.141.703.44
RandomSubSpace0.911.841.222.44
EnsembleSelection0.781.601.352.42
SimpleLogistic0.410.830.921.84
DecisionTable0.982.061.863.87
DataNearBalancedND0.881.761.422.84
RacedIncrementalLogitBoost0.631.591.683.61
SimpleCart0.631.261.132.25
LogitBoost0.430.871.232.47
ND0.881.761.422.84
BayesNet0.511.021.022.10
ClassBalancedND0.881.761.422.84
OrdinalClassClassifier0.881.761.422.84
END0.881.761.422.84