Prediction of RNA-Binding Proteins by Voting Systems

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