An Analytic Hierarchy Model for Classification Algorithms Selection in Credit Risk Analysis

Table 2

Evaluation results of Australian credit dataset.

Australian	Acc	TPR	TNR	Precision	𝐹-measure	AUC	Kaps	MAE	Training time	Test time
BNK	0.852	0.798	0.896	0.860	0.828	0.913	0.6986	0.1702	0.0125	0.0009
NBS	0.772	0.586	0.922	0.857	0.696	0.896	0.5244	0.2253	0.0055	0.0014
LRN	0.862	0.866	0.859	0.831	0.848	0.932	0.7224	0.1906	0.0508	0.0005
J48	0.835	0.795	0.867	0.827	0.811	0.834	0.6642	0.1956	0.0398	0.0002
NBTree	0.8333	0.779	0.877	0.836	0.806	0.885	0.6603	0.2195	1.3584	0.0008
IB1	0.794	0.775	0.809	0.765	0.770	0.792	0.5839	0.2058	0.0005	0.0473
IBK	0.794	0.775	0.809	0.765	0.770	0.792	0.5839	0.2067	0.0003	0.0164
SMO	0.885	0.925	0.799	0.787	0.850	0.862	0.7116	0.1449	0.3744	0.0008
RBF	0.830	0.752	0.893	0.849	0.798	0.895	0.6528	0.2463	0.0683	0.0009
MLP	0.825	0.818	0.830	0.794	0.806	0.899	0.6460	0.1807	5.6102	0.0014