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Comprehensible credit scoring models using rule extraction from support vector machines

Martens, David, Baesens, Bart, Van Gestel, Tony and Vanthienen, Jan (2007) Comprehensible credit scoring models using rule extraction from support vector machines European Journal of Operational Research, 183, (3), pp. 1466-1476. (doi:10.1016/j.ejor.2006.04.051).

Record type: Article


In recent years, support vector machines (SVMs) were successfully applied to a wide range of applications. However, since the classifier is described as a complex mathematical function, it is rather incomprehensible for humans. This opacity property prevents them from being used in many real-life applications where both accuracy and comprehensibility are required, such as medical diagnosis and credit risk evaluation. To overcome this limitation, rules can be extracted from the trained SVM that are interpretable by humans and keep as much of the accuracy of the SVM as possible. In this paper, we will provide an overview of the recently proposed rule extraction techniques for SVMs and introduce two others taken from the artificial neural networks domain, being Trepan and G-REX. The described techniques are compared using publicly available datasets, such as Ripley’s synthetic dataset and the multi-class iris dataset. We will also look at medical diagnosis and credit scoring where comprehensibility is a key requirement and even a regulatory recommendation. Our experiments show that the SVM rule extraction techniques lose only a small percentage in performance compared to SVMs and therefore rank at the top of comprehensible classification techniques.

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Published date: 16 December 2007
Keywords: credit scoring, classification, support vector machine, rule extraction


Local EPrints ID: 51706
ISSN: 0377-2217
PURE UUID: 04bfd76d-c655-4702-8fae-bef4fe0b26f1

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Date deposited: 06 Jun 2008
Last modified: 17 Jul 2017 14:48

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Author: David Martens
Author: Bart Baesens
Author: Tony Van Gestel
Author: Jan Vanthienen

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