Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-23151-3_13
Title: Rule Extraction from Neural Networks and Support Vector Machines for Credit Scoring
Authors: Setiono, R. 
Baesens, B.
Martens, D.
Issue Date: 2012
Source: Setiono, R.,Baesens, B.,Martens, D. (2012). Rule Extraction from Neural Networks and Support Vector Machines for Credit Scoring. Intelligent Systems Reference Library 25 : 299-320. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-23151-3_13
Abstract: In 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. © Springer-Verlag Berlin Heidelberg 2011.
Source Title: Intelligent Systems Reference Library
URI: http://scholarbank.nus.edu.sg/handle/10635/77912
ISBN: 9783642231506
ISSN: 18684394
DOI: 10.1007/978-3-642-23151-3_13
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

1
checked on Feb 13, 2018

Page view(s)

59
checked on Feb 17, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.