Toward a General-Purpose Heterogeneous Ensemble for Pattern Classification

Table 4

Performance obtained on the different image datasets using LPQ as texture descriptor.

LPQ	Datasets (AUC)														Av	RA
	PS	VI	CH	SM	HI	BR	PR	HE	LO	TR	PI	RN	PA	LE
SVM	0.9039	0.9492	0.9999	0.9986	0.9138	0.9565	0.8618	0.9757	0.9764	0.9767	0.9071	0.9532	0.8834	0.9897	0.9461	8.4
RS-; SVM	0.8951	0.9485	0.9999	0.9988	0.9251	0.9568	0.8727	0.9786	0.9809	0.9817	0.9128	0.9531	0.8854	0.9891	0.9485	7.5
GPC	0.9020	0.9282	0.9991	0.9985	0.9199	0.9720	0.8883	0.9793	0.9891	0.9934	0.9073	0.9439	0.8867	0.9782	0.9490	7.4
RS_AB	0.9013	0.9417	0.9998	0.9989	0.8783	0.9671	0.8843	0.9781	0.9868	0.9907	0.9255	0.9478	0.8777	0.9826	0.9472	7.9
RS_RB	0.8994	0.9393	0.9992	0.9978	0.9120	0.9711	0.8999	0.9741	0.9800	0.9889	0.9116	0.9562	0.8806	0.9799	0.9493	8.6
DL1	0.8701	0.9382	0.9994	0.9982	0.9083	0.9684	0.8758	0.9815	0.9847	0.9873	0.9110	0.9537	0.8858	0.9819	0.9460	9.0
DL2	0.8081	0.9379	0.9989	0.9979	0.9025	0.9682	0.8745	0.9813	0.9851	0.9852	0.9033	0.9550	0.8783	0.9853	0.9401	10.2
DL3	0.8717	0.9401	0.9990	0.9983	0.9097	0.9647	0.8694	0.9813	0.9861	0.9854	0.9038	0.9666	0.8785	0.9833	0.9456	9.3
S_D	0.8864	0.9415	0.9997	0.9982	0.9165	0.9687	0.8807	0.9830	0.9871	0.9885	0.9118	0.9594	0.8894	0.9848	0.9497	6.3
E1	0.9045	0.9345	0.9994	0.9989	0.9137	0.9726	0.8884	0.9794	0.9899	0.9931	0.9202	0.9469	0.8860	0.9807	0.9506	6.3
E2	0.9065	0.9441	0.9995	0.9991	0.9168	0.9742	0.8942	0.9793	0.9883	0.9932	0.9219	0.9574	0.8910	0.9834	0.9535	4.2
E3	0.9103	0.9467	0.9999	0.9990	0.9238	0.9716	0.8968	0.9805	0.9891	0.9927	0.9228	0.9581	0.8981	0.9867	0.9554	3.3
E4	0.9097	0.9492	1.000	0.9990	0.9258	0.9714	0.8978	0.9825	0.9902	0.9926	0.9235	0.9635	0.8998	0.9870	0.9566	2.2