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一种可解释的信用风险评估模型,具有边界样本识别.
Runchi Zhang1, Iris Li2, Zhiyuan Ding3
1School of Economics, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu, China.
PeerJ. Computer science
|September 24, 2025
概括
一种新的信用风险模型,即具有识别边界样本 (IAIBS) 的可解释信用风险评估模型,通过区分噪声和边界样本来提高准确性. 这种可解释的模型在信用风险预测方面显著优于现有的方法.
科学领域:
- 信用风险建模 信用风险建模
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 解释性对于可靠和符合信用风险评估模型至关重要.
- 从边界样本区分噪声对于提高预测准确度至关重要.
研究的目的:
- 引入一种新的,可解释的信用风险评估模型 (IAIBS).
- 通过有效处理噪音和边界样本,提高信用风险预测的准确性.
主要方法:
- 使用逻辑回归子模型来解释可解释性.
- 使用ARPD算法来识别和过噪音/边界样本.
- 在边界样本上训练一个深度学习子模型,并使用聚类进行最终预测.
主要成果:
- 在四个公共数据集中,IAIBS模型显著超过了11个基线模型.
- 获得了高的曲线下面积 (AUC) 评分,证明了强大的预测性能.
- 展示了强大的概括能力和每个模型模块的积极贡献.
结论:
- 在信用风险评估中,IAIBS模型提供了卓越的性能和可解释性.
- 有效识别边界样本可以提高预测的准确性.
- 该模型提供了关键预测因素和结果的清晰解释.
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