Table 2.

The results over imbalanced, trimmed and balanced data.

Features	Dataset	AUC	Sensitivity	Specificity
Group 1	Imbalanced	0.71815	0.23618	0.95589
	Trimmed	0.69259	0.60103	0.67216
	Balanced	0.72944	0.62285	0.70575
Group 2	Imbalanced	0.74139	0.26960	0.96100
	Trimmed	0.74575	0.73217	0.62533
	Balanced	0.77802	0.69670	0.71086
Group 3	Imbalanced	0.81745	0.37526	0.95015
	Trimmed	0.81670	0.72653	0.74426
	Balanced	0.84647	0.76836	0.76798
Group 4	Imbalanced	0.80099	0.27180	0.96929
	Trimmed	0.79362	0.72478	0.71542
	Balanced	0.83079	0.73978	0.75002

The imbalanced data includes all examples in the original datasets; the trimmed data owns all positive examples and randomly selected negative examples, with a 1:1 ratio of positive to negative examples; the balanced datasets are generated by Sub-EnClassifiers with resampling technique.