Table 2.

The standard deviation of the 34 algorithms.

Algorithm	Standard deviation
	Basic training dataset		Independent test dataset
	ACC (%)	MCC (%)	ACC (%)	MCC (%)
AdaBoostM1	0.61	1.16	1.00	1.94
J48	0.88	1.76	1.42	2.84
IBk	0.52	1.01	1.18	2.21
MultiClassClassifier	0.60	1.21	1.04	2.09
PART	0.55	1.25	1.26	2.54
MultilayerPerceptron	1.26	2.52	2.22	3.04
KStar	0.72	1.41	1.07	2.00
Bagging	0.76	1.51	0.43	0.88
NBTree	0.82	1.64	2.04	4.09
Decorate	0.73	1.47	1.16	2.25
RandomForest	0.67	1.32	0.62	1.25
JRip	0.48	0.96	2.25	4.43
RandomCommittee	0.51	0.99	1.23	2.59
FilteredClassifier	1.11	2.22	1.16	2.32
ClassificationViaRegression	0.96	1.91	0.80	1.57
Dagging	0.70	1.38	1.00	2.00
AttributeSelectedClassifier	0.85	1.71	0.66	1.40
REPTree	0.71	1.46	1.32	2.66
SMO	0.55	1.10	1.06	2.11
J48graft	1.06	2.12	1.40	2.81
Ridor	1.01	2.14	1.70	3.44
RandomSubSpace	0.91	1.84	1.22	2.44
EnsembleSelection	0.78	1.60	1.35	2.42
SimpleLogistic	0.41	0.83	0.92	1.84
DecisionTable	0.98	2.06	1.86	3.87
DataNearBalancedND	0.88	1.76	1.42	2.84
RacedIncrementalLogitBoost	0.63	1.59	1.68	3.61
SimpleCart	0.63	1.26	1.13	2.25
LogitBoost	0.43	0.87	1.23	2.47
ND	0.88	1.76	1.42	2.84
BayesNet	0.51	1.02	1.02	2.10
ClassBalancedND	0.88	1.76	1.42	2.84
OrdinalClassClassifier	0.88	1.76	1.42	2.84
END	0.88	1.76	1.42	2.84