Rule Extraction from Neural Networks and Support Vector Machines for Credit Scoring

Abstract

AbstractIn this chapter we describe how comprehensible rules can be extracted from artificial neural networks (ANN) and support vector machines (SVM). ANN and SVM are two very popular techniques for pattern classification. In the business intelligence application domain of credit scoring, they have been shown to be effective tools for distinguishing between good credit risks and bad credit risks. The accuracy obtained by these two techniques is often higher than that from decision tree methods. Unlike decision tree methods, however, the classifications made by ANN and SVM are difficult to understand by the end-users as outputs from ANN and SVM are computed as nonlinear mapping of the input data attributes. We describe two rule extraction methods that we have developed to overcome this difficulty. These rule extraction methods enable the users to obtain comprehensible propositional rules from ANN and SVM. Such rules can be easily verified by the domain experts and would lead to a better understanding about the data in hand.