Toward a General-Purpose Heterogeneous Ensemble for Pattern Classification

Table 6

Performance on the different data mining datasets.

	Datasets (AUC)											Av	RA
	BR	HE	PI	SP	SO	IO	LI	HA	VO	AU	TR
SVM	0.9941	0.8809	0.824	0.9708	0.9517	0.9814	0.7558	0.7012	0.9855	0.9164	0.714	0.8796	7.3077
RS-SVM	0.9931	0.9076	0.8221	0.9771	0.9591	0.9795	0.7411	0.6399	0.9853	0.9221	0.6931	0.8745	8.6923
GPC	0.9924	0.9024	0.827	0.979	0.9409	0.9713	0.729	0.6804	0.9882	0.9267	0.7295	0.8788	8.000
RS_AB	0.991	0.9101	0.8229	0.988	0.9371	0.9788	0.7581	0.6727	0.9887	0.9313	0.735	0.8831	7.000
RS_RB	0.9925	0.9169	0.8208	0.9873	0.9334	0.9851	0.7664	0.6071	0.9884	0.9326	0.674	0.8731	7.3846
DL1	0.9943	0.8852	0.8252	0.966	0.8794	0.9222	0.7541	0.6751	0.9795	0.9155	0.7338	0.8664	8.7692
DL2	0.9941	0.8754	0.8149	0.9691	0.8789	0.9242	0.7478	0.6679	0.9808	0.9088	0.7318	0.8631	10.3077
DL3	0.9943	0.8941	0.8193	0.9684	0.8501	0.9022	0.6966	0.6537	0.9787	0.9154	0.7351	0.8553	10.2308
S_D	0.9942	0.883	0.8238	0.9683	0.8781	0.9297	0.751	0.6772	0.9813	0.9186	0.7357	0.8674	8.6154
E1	0.992	0.9096	0.8277	0.9856	0.9426	0.9772	0.7532	0.6868	0.9896	0.9331	0.7372	0.885	6.000
E2	0.9924	0.9124	0.8285	0.988	0.9426	0.9817	0.7727	0.6724	0.9897	0.935	0.7257	0.8856	5.3846
E3	0.9933	0.9141	0.8288	0.9873	0.9508	0.9819	0.7723	0.6726	0.989	0.9343	0.7258	0.8864	5.0769
E4	0.9934	0.9113	0.8294	0.9862	0.942	0.9805	0.7717	0.6794	0.9895	0.9339	0.7297	0.8861	5.2308