提高企业信用风险评估与级联式多层次图表表示学习学习
Lingyun Song1, Haodong Li1, Yacong Tan1
1School of Computer Science, Northwestern Polytechnical University, Xi'an, 710129, China; Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi'an, 710129, China.
概括
本研究引入了一个新的框架,MS-CGNN,通过分析复杂的高阶关系,超越简单的对连接,改进企业信用风险评估. 这种方法提高了准确性,特别是当传统数据有限时.
科学领域:
- 计算金融是指计算金融.
- 图表 机器学习 机器学习
- 风险管理 风险管理
背景情况:
- 企业信用风险 (ECR) 评估对于投资和监管至关重要.
- 传统的ECR方法依赖于可访问的信用指标,这些指标往往无法用于中小企业.
- 现有的ECR图形学习方法忽略了复杂的,高阶的企业关系.
研究的目的:
- 解决传统ECR评估和现有的图形方法的局限性.
- 提出一个新的框架,MS-CGNN,用于增强企业代表性学习.
- 通过使用多结构图形学习来结合高阶企业关系.
主要方法:
- 开发了一个多结构级联图神经网络 (MS-CGNN) 框架.
- 利用对关系的知识图和高阶关系 (同质和异质) 的超图.
- 引入了类型依赖的超边缘权重矩阵,用于异质的超图卷积.
主要成果:
- MS-CGNN有效地增强了企业代表性学习.
- 该框架利用多种不同的图形结构 (对,同质,异质的超图).
- 在真实世界ECR数据集上实现了最先进的性能.
结论:
- MS-CGNN为ECR评估提供了强大的解决方案,克服了数据缺陷.
- 纳入高级关系可以显著提高风险评估的准确性.
- 拟议的方法提供了一种更全面的方法来理解企业的相互依赖.
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