Credit risk assessment based on rough set theory and fuzzy support vector machine

DOI

10.2991/iske.2007.157How to use a DOI?

Keywords

Rough sets. FSVM. Credit risk assessment.

Abstract

In this paper, a hybrid intelligent system, combining rough set approach and fuzzy support vector machine (FSVM), is applied to the study of credit risk assessment in commercial banks. We can get reduced information table, which implies that the number of evaluation criteria such as financial ratios and qualitative variables is reduced with no information loss through rough set approach. And then, this reduced information table is used to develop classification rules and train FSVM. The rationale of our hybrid system is using rules developed by rough sets for an object that matches any of the rules and FSVM for an object that does not match any of them.

Copyright

© 2007, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jianguo Zhou
AU  - Jiming Tian
PY  - 2007/10
DA  - 2007/10
TI  - Credit risk assessment based on rough set theory and fuzzy support vector machine
BT  - Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007)
PB  - Atlantis Press
SP  - 926
EP  - 931
SN  - 1951-6851
UR  - https://doi.org/10.2991/iske.2007.157
DO  - 10.2991/iske.2007.157
ID  - Zhou2007/10
ER  -