Rule extraction from minimal neural networks for credit card screening

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While feedforward neural networks have been widely accepted as effective tools for solving classification problems, the issue of finding the best network architecture remains unresolved, particularly so in real-world problem settings. We address this issue in the context of credit card screening, where it is important to not only find a neural network with good predictive performance but also one that facilitates a clear explanation of how it produces its predictions. We show that minimal neural networks with as few as one hidden unit provide good predictive accuracy, while having the added advantage of making it easier to generate concise and comprehensible classification rules for the user. To further reduce model size, a novel approach is suggested in which network connections from the input units to this hidden unit are removed by a very straightaway pruning procedure. In terms of predictive accuracy, both the minimized neural networks and the rule sets generated from them are shown to compare favorably with other neural network based classifiers. The rules generated from the minimized neural networks are concise and thus easier to validate in a real-life setting.

10.1142/S0129065711002821

0129-0657

265-276

Setiono, R.

afc2459a-ab0e-4716-baf9-75a5d20e7409

Baesens, B.

f7c6496b-aa7f-4026-8616-ca61d9e216f0

Mues, C.

07438e46-bad6-48ba-8f56-f945bc2ff934