Developing an Intelligent Model Based on Fuzzy Multilayer Neural Network for Improving Credit Risk Management under Uncertain Conditions

Table 2

Australian credit information collection.

Attributes	Type	Amounts	Values (previous)
1	Discrete	0.1	a, b
2	Continuous	80.25–13.75	80.25–13.75
3	Continuous	0–28	0–28
4	Discrete	1, 2, 3	P, g, g, g
5	Discrete	1, 2, 3, …, 14	ff, d, i, k, j, aa, m, c, w, e, q, r, cc, x
6	Discrete	1, 2, 3, …, 9	ff, dd, j, bb, v, n, o, h, z
7	Continuous	0–28.5	0–28.5
8	Discrete	0, 1	t, f
9	Discrete	0, 1	t, f
10	Continuous	0–67	0–67
11	Discrete	0, 1	t, f
12	Discrete	1, 2, 3	s, g, p
13	Continuous	0–2000	0–2000
14	Continuous	0–100000	0–100000
Class	Discrete	0, 1	−, +