Article ID: | iaor20012645 |
Country: | South Korea |
Volume: | 25 |
Issue: | 3 |
Start Page Number: | 125 |
End Page Number: | 135 |
Publication Date: | Sep 2000 |
Journal: | Journal of the Korean ORMS Society |
Authors: | Lee Hoon-Young, Park Ki-Nam, Park Sang-Kuk |
Keywords: | credit management |
Many different statistical and artificial intelligent techniques have been applied to improve the predictability of credit rating. Hybrid models and systems have also been developed by effectively combining different modeling processes or combining the outcomes of individual models. In this paper, we introduced the rough set theory and developed a hybrid credit rating system that combines individual outcomes in terms of rough set theory. An experiment was conducted to compare the prediction capability of the system with those of other methods. The proposed system based on rough set method outperformed the others.