RULE EXTRACTION FROM MINIMAL NEURAL NETWORKS FOR CREDIT CARD SCREENING

Author:

SETIONO RUDY1,BAESENS BART2,MUES CHRISTOPHE3

Affiliation:

1. School of Computing, National University of Singapore, 13 Computing Drive, Singapore 117417, Republic of Singapore

2. Department of Applied Economic Sciences, K. U. Leuven, Naamsestraat 69, B-3000 Leuven, Belgium

3. School of Management, University of Southampton, Southampton, SO17 1BJ, United Kingdom

Abstract

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.

Publisher

World Scientific Pub Co Pte Lt

Subject

Computer Networks and Communications,General Medicine

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