A Pruning Neural Network Model in Credit Classification Analysis

Table 2

Attributes for evaluating credit risk in the Japanese 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		u, y, l, t
A5	Categorical		g, p, gg
A6	Categorical		c, d, cc, i, j, k, m, r, q, w, x, e, aa, ff
A7	Categorical		v, h, bb, j, n, z, dd, ff, o
A8	Numerical	0–28.5	0–28.5
A9	Categorical		t, f
A10	Categorical		t, f
A11	Numerical	0–67	0–67
A12	Categorical		t, f
A13	Categorical		g, p, s
A14	Numerical	0–2000	0–2000
A15	Numerical	0–100,000	0–100,000
Class	Categorical