The Optimal Re-sampling Strategy for a Risk Assessment Model

机译：风险评估模型的最优重采样策略

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The global economic environment is changing rapidly. Consequently, the financial risks of banks or financial institutions are also increased. Banks or financial institutions often utilize various classification methods to construct risk assessment models to determine whether to grant loans to a corporation or an individual. It is often found that the data used to construct a risk assessment model are imbalanced. That is, the number of default is significantly smaller than the number of non-default. In this case, most classification methods fail to construct an accurate risk assessment model since the classification methods are subjected to the imbalanced data. The try-and-error method is often utilized to balance the sample sizes for default and non-default classes. However, the try-and-error method is costly and the sampling strategy determined by the try-and-error method may not effectively classify the imbalanced data. Therefore, this study aims to develop an optimal re-sampling strategy using design of experiments (DOE) and dual response surface methodology (DRS). The proposed method can be employed for any classification method to develop a risk assessment model. The effectiveness of the proposed procedure is verified using a real case from a Taiwanese financial institution.

机译：全球经济环境正在迅速变化。因此，银行或金融机构的金融风险也增加了。银行或金融机构通常利用各种分类方法来构建风险评估模型，以确定是否向公司或个人授予贷款。经常发现，用于构建风险评估模型的数据是不平衡的。也就是说，默认数量明显小于非默认数量。在这种情况下，大多数分类方法都无法建立准确的风险评估模型，因为分类方法要承受不平衡的数据。尝试错误法通常用于平衡默认类和非默认类的样本大小。但是，尝试错误方法的成本很高，并且通过尝试错误方法确定的采样策略可能无法有效地对不平衡数据进行分类。因此，本研究旨在利用实验设计（DOE）和双重响应面方法（DRS）开发一种最佳的重采样策略。所提出的方法可以用于任何分类方法来开发风险评估模型。该程序的有效性通过台湾一家金融机构的真实案例进行了验证。

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《ISSAT international conference on reliability quality in design》|2012年|293-295|共3页
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Tong L. I.; Wei W. Y.; Wu P. Y.;
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Risk assessment; Re-sampling strategy; Imbalanced data; Design of Experiments; Dual Response Surface Methodology;

机译：风险评估;重采样策略;数据不平衡;实验设计;双重响应面方法;

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The Optimal Re-sampling Strategy for a Risk Assessment Model

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