Credit risk assessment based on rough set theory and fuzzy support vector machine
- https://doi.org/10.2991/iske.2007.157How to use a DOI?
- Rough sets. FSVM. Credit risk assessment.
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.
- © 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/).
Cite this article
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 - https://doi.org/10.2991/iske.2007.157 ID - Zhou2007/10 ER -