신경망 분리모형과 사례기반추론을 이용한기업 신용 평가Corporate Credit Rating using Partitioned Neural Network and Case-Based Reasoning

The corporate credit rating represents an assessment of the relative level of risk associated with the timely payments required by the debt obligation. In this study, the corporate credit rating model employs artificial intelligence methods including Neural Network (NN) and Case-Based Reasoning (CBR). At first we suggest three classification models, as partitioned neural networks, all of which convert multi-group classification problems into two group classification ones: Ordinal Pairwise Partitioning (OPP) model, binary classification model and simple classification model. The experimental results show that the partitioned NN outperformed the conventional NN. In addition, we put to use CBR that is widely used recently as a problem-solving and learning tool both in academic and business areas. With an advantage of the easiness in model design compared to a NN model, the CBR model proves itself to have good classification capability through the highest hit ratio in the corporate credit rating.
Publisher
한국데이타베이스학회
Issue Date
2007-06
Language
KOR
Citation

JOURNAL OF INFORMATION TECHNOLOGY APPLICATIONS & MANAGEMENT, v.14, no.2, pp.151 - 168

ISSN
1598-6284
URI
http://hdl.handle.net/10203/8176
Appears in Collection
KGSF-Journal Papers(저널논문)
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