The Hybrid Systems for Credit Rating

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Although numerous studies demonstrate that one technique outperforms the others for a given data set, it is hard to tell a priori which of these techniques will be the most effective to solve a specific problem. It has been suggested that the better approach to classification problem might be to integrate several different forecasting techniques by combining their results. The issues of interest are how to integrate different modeling techniques to increase the predictive performance. This paper proposes the post-model integration method, which tries to find the best combination of the results provided by individual techniques. To get the optimal or near optimal combination of different prediction techniques, Genetic Algorithms (GAs) are applied, which are particularly suitable for multi-parameter optimization problems with an object function subject to numerous hard and soft constraints. This study applies three individual classification techniques (Discriminant analysis, Logit model and Neural Networks) as base models for the corporate failure prediction. The results of composite predictions are compared with the individual models. Preliminary results suggests that the use of integrated methods improve the performance of business classification.
Publisher
한국경영과학회
Issue Date
1997-09
Language
English
Description

This article is confirmed to be submitted through the review and edition of the Korean Operations Research and Management Science Society. Please enter the title (Journal/Proceedings), volume, number, and pages properly when citing the article.

Citation

한국경영과학회지, v.22, no.3, pp.163 - 173

ISSN
1225-1119
URI
http://hdl.handle.net/10203/4212
Appears in Collection
MT-Journal Papers(저널논문)
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