Classifying Imbalanced Data Sets by a Novel RE-Sample and Cost-Sensitive Stacked Generalization Method

Table 8

Results of other methods in terms of AGeoMean.

Data set	NB	C4.5	KNN	Cost-NB	Ada-NB	Ada-Cost-NB	Bag- NB	Bag-Cost- NB	Stacking-log	Stacking-Cost-log
wisconsin	0.961	0.960	0.961	0.962	0.963	0.962	0.962	0.960	0.967	0.967
pima	0.768	0.743	0.706	0.734	0.767	0.736	0.775	0.729	0.770	0.726
haberman	0.642	0.680	0.601	0.718	0.660	0.694	0.653	0.721	0.613	0.663
Vehicle1	0.690	0.721	0.728	0.658	0.722	0.669	0.690	0.654	0.709	0.740
Vehicle0	0.666	0.929	0.934	0.658	0.681	0.663	0.664	0.663	0.924	0.936
segment0	0.859	0.986	0.996	0.850	0.859	0.871	0.863	0.854	0.995	0.995
yeast-0-3-5-9_vs_7-8	0.715	0.706	0.782	0.709	0.713	0.737	0.703	0.721	0.700	0.770
yeast-0-2-5-6_vs_3-7-8-9	0.856	0.778	0.855	0.857	0.856	0.855	0.856	0.854	0.839	0.866
vowel0	0.929	0.981	1.000	0.909	0.966	0.948	0.931	0.910	1.000	1.000
led7digit-0-2-4-5-6-7-8-9_vs_1	0.853	0.874	0.818	0.869	0.863	0.870	0.845	0.850	0.846	0.874
Glass2	0.511	0.701	0.695	0.493	0.511	0.551	0.539	0.515	0.000	0.744
cleveland-0_vs_4	0.927	0.845	0.841	0.943	0.783	0.914	0.918	0.910	0.918	0.918
glass-0-1-6_vs_5	0.954	0.932	0.894	0.954	0.954	0.923	0.923	0.919	0.902	0.932
car-good	0.000	0.000	0.763	0.878	0.747	0.870	0.000	0.873	0.769	0.890
flare-F	0.840	0.000	0.705	0.842	0.751	0.795	0.836	0.842	0.000	0.845
car-vgood	0.743	0.934	0.793	0.986	0.953	0.956	0.725	0.980	0.974	0.990
abalone-17_vs_7-8-9-10	0.736	0.655	0.745	0.716	0.650	0.672	0.734	0.711	0.641	0.804
Mean	0.744	0.731	0.813	0.808	0.788	0.805	0.742	0.8039	0.739	0.862