Toward a General-Purpose Heterogeneous Ensemble for Pattern Classification

Table 3

Performance (AUC) obtained in different image datasets using LTP as texture descriptor.

LTP	Datasets (AUC)														Av	RA
	PS	VI	CH	SM	HI	BR	PR	HE	LO	TR	PI	RN	PA	LE
SVM	0.9144	0.9349	1.000	0.9975	0.9156	0.9692	0.8968	0.9814	0.9949	0.9926	0.9286	0.9696	0.8903	0.9792	0.9546	7.0
RS-SVM	0.9071	0.9352	1.000	0.9976	0.9195	0.9763	0.9030	0.9826	0.9950	0.9924	0.9316	0.9713	0.8944	0.9807	0.9562	5.9
GPC	0.9086	0.9131	0.9997	0.9971	0.9198	0.9789	0.8865	0.9816	0.9964	0.9930	0.9090	0.9769	0.8968	0.9752	0.9523	8.1
RS_AB	0.9121	0.9254	0.9998	0.9974	0.8924	0.9810	0.9079	0.9813	0.9965	0.9953	0.9242	0.9771	0.8959	0.9738	0.9543	7.0
RS_RB	0.9110	0.9293	0.9999	0.9953	0.9136	0.9739	0.8886	0.9806	0.9969	0.9955	0.9178	0.9900	0.8940	0.9738	0.9543	7.6
DL1	0.8927	0.9173	0.9999	0.9952	0.9072	0.9811	0.8486	0.9801	0.9962	0.9965	0.9147	0.9837	0.8865	0.9811	0.9486	8.7
DL2	0.8965	0.9220	0.9998	0.9956	0.8945	0.9815	0.8780	0.9806	0.9956	0.9959	0.9061	0.9878	0.8869	0.7900	0.9365	9.4
DL3	0.7802	0.9239	0.9999	0.9963	0.9082	0.9815	0.8779	0.9812	0.9958	0.9962	0.9014	0.9916	0.8895	0.9525	0.9412	8.5
S_D	0.8985	0.9244	1.000	0.9958	0.9143	0.9835	0.8783	0.9818	0.9958	0.9966	0.9151	0.9916	0.8960	0.9806	0.9537	6.0
E1	0.9130	0.9196	0.9997	0.9974	0.9162	0.9812	0.8999	0.9816	0.9962	0.9945	0.9191	0.9798	0.8983	0.9748	0.9551	7.2
E2	0.9165	0.9337	0.9998	0.9973	0.9184	0.9809	0.9007	0.9837	0.9969	0.9960	0.9238	0.9884	0.9030	0.9768	0.9583	5.1
E3	0.9165	0.9361	1.000	0.9975	0.9229	0.9816	0.9090	0.9843	0.9970	0.9968	0.9313	0.9835	0.9080	0.9796	0.9603	2.8
E4	0.9164	0.9372	1.000	0.9975	0.9235	0.9824	0.9059	0.9851	0.9973	0.9971	0.9318	0.9864	0.9093	0.9808	0.9608	2.2