| Summary: | 碩士 === 國立交通大學 === 工業工程與管理學系 === 99 === The main revenue of financial institutions comes from the interest they charge to their enterprises customers. But some customers may not be able to pay their debts back, so financial institutions needs to adopt some risk assessment models to measure this credit risk. Many risk assessment models have been developed to deal with the credit risk; most of them used only one stage classifier, but when those methods have to deal with financial data, which was divided into two categories with large numbers of normal instances and small number of default instances, there may be a large gap in accuracy between these two categories. Too many features used in a risk assessment model without feature selection may cause the problem of Overfitting. This study construction a two-stage risk assessment model using Group Method of Data Handling (GMDH) method and decision tree method. In the first stage, this study designs a GMDH-based feature selection method. A feature ranking method is used to rank the entire feature first, and then uses a feature selection method to choose the most appropriate features into construction the GMDH model. In the second stage a decision tree is used to identify the wrong classification instances and revise them into the right ones. In the end two credit risk data in UCI Repository of Machine Learning database and a real case from a Taiwanese financial institution are used to demonstrate the accurate of the proposed two-stage risk assessment model. This study also compares to other references to see that our study would have the same or better result than other models.
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