A Novel Ensemble Credit Scoring Model Based on Extreme Learning Machine and Generalized Fuzzy Soft Sets

Table 7

Classification outcomes from all data sets for different performance measures with various combinations.

Data set	Performance measurement	Other feature selection methods with GFSS-based combination				Traditional combination methods with AEnet-based feature selection				EGHE
		Cost-sensitive	GA	IGR	Enet	WAVG	MajVot	WVOT	FSS
Germany	AUC	0.786	0.792	0.777	0.781	0.777	0.786	0.789	0.771	0.823
	HM	0.286	0.177	0.226	0.238	0.222	0.285	0.246	0.305	0.325
	BS	0.166	0.208	0.197	0.182	0.181	0.183	0.192	0.157	0.184
	ACC	0.842	0.862	0.833	0.827	0.845	0.859	0.836	0.844	0.886
Australia	AUC	0.921	0.927	0.933	0.928	0.921	0.922	0.932	0.928	0.945
	HM	0.667	0.519	0.573	0.628	0.637	0.637	0.632	0.653	0.659
	BS	0.092	0.145	0.173	0.103	0.101	0.112	0.107	0.097	0.095
	ACC	0.872	0.878	0.882	0.863	0.872	0.873	0.876	0.874	0.895
Japan	AUC	0.923	0.928	0.918	0.932	0.918	0.927	0.928	0.925	0.937
	HM	0.650	0.492	0.566	0.618	0.602	0.642	0.617	0.648	0.660
	BS	0.099	0.157	0.174	0.109	0.121	0.112	0.116	0.103	0.098
	ACC	0.871	0.862	0.861	0.868	0.853	0.864	0.873	0.868	0.904
Iran	AUC	0.791	0.778	0.781	0.767	0.777	0.779	0.783	0.778	0.802
	HM	0.279	0.150	0.217	0.162	0.283	0.108	0.107	0.294	0.303
	BS	0.042	0.069	0.056	0.049	0.044	0.048	0.049	0.044	0.059
	ACC	0.883	0.881	0.874	0.861	0.867	0.885	0.877	0.883	0.915
Bene 1	AUC	0.843	0.812	0.822	0.821	0.882	0.879	0.886	0.888	0.881
	HM	0.396	0.263	0.338	0.258	0.324	0.441	0.385	0.447	0.351
	BS	0.161	0.240	0.256	0.197	0.188	0.159	0.198	0.154	0.173
	ACC	0.881	0.872	0.864	0.872	0.872	0.872	0.865	0.876	0.896
Bene 2	AUC	0.921	0.838	0.858	0.868	0.878	0.844	0.876	0.883	0.888
	HM	0.537	0.401	0.478	0.489	0.505	0.436	0.432	0.496	0.506
	BS	0.091	0.136	0.168	0.122	0.102	0.115	0.112	0.103	0.114
	ACC	0.878	0.860	0.857	0.868	0.881	0.875	0.882	0.884	0.898
Shuttle	AUC	0.896	0.914	0.914	0.922	0.897	0.919	0.917	0.906	0.943
	HM	0.653	0.510	0.562	0.625	0.620	0.635	0.622	0.636	0.658
	BS	0.091	0.143	0.170	0.103	0.099	0.110	0.104	0.096	0.095
	ACC	0.852	0.866	0.862	0.858	0.849	0.870	0.862	0.851	0916
Skin_segment	AUC	0.902	0.913	0.899	0.926	0.896	0.922	0.915	0.903	0.936
	HM	0.637	0.486	0.555	0.615	0.586	0.638	0.607	0.631	0.659
	BS	0.096	0.154	0.171	0.107	0.119	0.110	0.113	0.098	0.098
	ACC	0.853	0.849	0.844	0.863	0.833	0.862	0.859	0.845	0.908
MiniBooNE	AUC	0.773	0.767	0.764	0.763	0.757	0.775	0.770	0.758	0.800
	HM	0.272	0.149	0.213	0.159	0.277	0.109	0.104	0.286	0.302
	BS	0.042	0.067	0.054	0.048	0.0439	0.047	0.047	0.041	0.059
	ACC	0.875	0.877	0.885	0.876	0.884	0.880	0.893	0.890	0.903
LC2017Q1	AUC	0.823	0.801	0.804	0.816	0.859	0.874	0.872	0.867	0.879
	HM	0.386	0.260	0.332	0.257	0.315	0.440	0.378	0.435	0.350
	BS	0.158	0.237	0.252	0.195	0.182	0.157	0.196	0.149	0.203
	ACC	0.861	0.888	0.877	0.875	0.871	0.878	0.871	0.873	0.912