A Novel Selective Ensemble Algorithm for Imbalanced Data Classification Based on Exploratory Undersampling

Table 5

Performance results of all methods based on 𝐹-measure evaluation metric. The values with boldface mean the best result.

Dataset	CART	RUSB	SMOB	UNBag	SMBag	AdaC	RAMO	RotF	Easy	RotE-un	RotEasy
Spambase	0.8947	0.8630	0.9201	0.9226	0.9233	0.9371	0.9351	0.9412	0.9454	0.9481	0.9565
Vote	0.9305	0.9356	0.9420	0.9391	0.9405	0.9320	0.9263	0.9319	0.9394	0.9367	0.9561
Wdbc	0.9001	0.9218	0.9481	0.9228	0.9375	0.9361	0.9567	0.9507	0.9538	0.9441	0.9531
Ionosphere	0.8261	0.8708	0.9250	0.8661	0.8688	0.8941	0.9138	0.9194	0.8942	0.9034	0.9373
Pima	0.5683	0.6750	0.6562	0.6607	0.6020	0.2789	0.6427	0.6399	0.6662	0.6421	0.7649
German	0.5134	0.6149	0.5621	0.5977	0.5319	0.2953	0.5344	0.5446	0.6114	0.6039	0.7027
Phoneme	0.7744	0.6770	0.8529	0.8075	0.8373	0.8423	0.8586	0.8468	0.8274	0.8196	0.8661
Haberman	0.3741	0.4549	0.4218	0.4736	0.3235	0.3412	0.3584	0.3402	0.4652	0.4621	0.5949
Vehicle	0.518	0.6567	0.6278	0.6359	0.5307	0.2638	0.5522	0.5432	0.6506	0.6571	0.7087
Cmc	0.3649	0.4472	0.3876	0.4693	0.3813	0.2981	0.3828	0.3615	0.4577	0.4630	0.5797
House	0.3899	0.5431	0.5532	0.5392	0.4525	0.3066	0.5044	0.3929	0.5792	0.5375	0.5745
Scrapie	0.2420	0.3302	0.2087	0.3423	0.3262	0.2578	0.2818	0.2380	0.3362	0.3317	0.3856
Yeast	0.6982	0.7878	0.7736	0.7302	0.7614	0.7663	0.7603	0.7322	0.7244	0.7220	0.7368
Mfeat_zer	0.7135	0.9059	0.9201	0.7388	0.8167	0.8502	0.9127	0.8735	0.8203	0.8478	0.8161
Mfeat_kar	0.7201	0.8955	0.9191	0.7042	0.8049	0.8364	0.9070	0.8465	0.8686	0.8910	0.8080
Satimage	0.5491	0.6081	0.7235	0.5704	0.6668	0.6749	0.7215	0.6753	0.5794	0.5825	0.5925
Abalone7	0.2407	0.3203	0.3289	0.3769	0.2759	0.1986	0.2543	0.1138	0.3824	0.3865	0.3985
Sick	0.8389	0.8703	0.8857	0.8054	0.8714	0.8750	0.8848	0.8670	0.8155	0.8159	0.8137
Cbands	0.7514	0.9275	0.9392	0.7419	0.8775	0.9024	0.9335	0.8979	0.8199	0.8533	0.7909
Ozone	0.1737	0.1510	0.2872	0.1998	0.2572	0.1472	0.1860	0.0740	0.2017	0.2043	0.2068
Average	0.5991	0.6728	0.6890	0.6522	0.6494	0.5917	0.6704	0.6365	0.6769	0.6776	0.7072