A Pruning Neural Network Model in Credit Classification Analysis

Table 1

Attributes for evaluating credit risk in the Australian credit dataset.

Attributes	Type	Values (after preprocessing)	Values (before preprocessing)
A1	Categorical		a, b
A2	Numerical	13.75–80.25	13.75–80.25
A3	Numerical	0–28	0–28
A4	Categorical		p, g, gg
A5	Categorical		ff, d, i, k, j, aa, m, c, w, e, q, r, cc, x
A6	Categorical		ff, dd, j, bb, v, n, o, h, z
A7	Numerical	0–28.5	0–28.5
A8	Categorical		t, f
A9	Categorical		t, f
A10	Numerical	0–67	0–67
A11	Categorical		t, f
A12	Categorical		s, g, p
A13	Numerical	0–2000	0–2000
A14	Numerical	0–100,000	0–100,000
Class	Categorical