Better classifiers for credit scoring : a comparison study between self organizing maps (SOM) and support vector machine (SVM)

Shahlaii Moghadam, A, Shalbafzadeh, A and Saraee, MH ORCID: https://orcid.org/0000-0002-3283-1912 2009, Better classifiers for credit scoring : a comparison study between self organizing maps (SOM) and support vector machine (SVM) , in: 3rd International Conference on Communications and information technology, 29-31 December 2009, Vouliagmeni, Athens, Greece.

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Abstract

Credit scoring has become an increasingly important area for financial institutions. Self Organizing Maps and Support Vector Machine are two techniques of data mining which are used in different applications of businesses. In this paper, we use descriptive variables in literatures and criteria which effect on credit of customers in Iran financial institutions. We will evaluate these variables with Multi Criteria Decision Making (MCDM) and take into account the psychological and sociology viewpoints of experts. Next We apply and compare SVM method against SOM method on the credit database. For comparing these two methods we use coincidence matrix and the Type I and Type II errors. We show that they are competitive and most significant in determining the risk of default on bank customers.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Proceedings ISBN: 9789604741465
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Journal or Publication Title: Proceedings of the 3rd International Conference on Communications and information technology
Publisher: ACM Digital Library
Related URLs:
Depositing User: Prof. Mo Saraee
Date Deposited: 20 Aug 2018 13:21
Last Modified: 13 Mar 2019 08:01
URI: http://usir.salford.ac.uk/id/eprint/48115

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