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

Table 3

Evaluation results of German credit dataset.

German	Acc	TPR	TNR	Precision	𝐹-measure	AUC	Kaps	MAE	Training time	Test time
BNK	0.725	0.360	0.881	0.565	0.440	0.740	0.2694	0.3410	0.0247	0.0011
NBS	0.755	0.507	0.861	0.610	0.554	0.785	0.3689	0.2904	0.0134	0.0034
LRN	0.771	0.493	0.890	0.658	0.564	0.790	0.4128	0.3153	0.1139	0.0005
J48	0.719	0.440	0.839	0.539	0.484	0.661	0.2940	0.3241	0.1334	0.0005
NBTree	0.726	0.380	0.874	0.564	0.454	0.734	0.2805	0.344	1.9339	0.0023
IB1	0.669	0.450	0.763	0.449	0.449	0.606	0.2127	0.3310	0.0020	0.1680
IBK	0.669	0.450	0.763	0.449	0.449	0.606	0.2127	0.3310	0.0002	0.0694
SMO	0.774	0.493	0.894	0.667	0.567	0.694	0.4187	0.2260	0.5861	0.0005
RBF	0.740	0.463	0.859	0.584	0.517	0.747	0.3421	0.3429	0.1694	0.0023
MLP	0.718	0.477	0.821	0.534	0.504	0.717	0.3075	0.2891	20.0513	0.0025