用图注意力与频域特征提升长期信贷风险预测稳定性
Incremental Hybrid Ensemble with Graph Attention and Frequency-Domain Features for Stable Long-Term Credit Risk Modeling
- 融合图注意力与频域特征构建动态关系网络
- 每周更新模型，性能自适应调整避免频繁重训
- 适合需要长期稳定预测的金融风控场景
长期贷款违约预测困难，因借款人行为变化及数据分布随时间漂移。本文提出HYDRA-EI，一种混合集成增量学习框架，通过多阶段特征处理，构建关系、交叉与频域特征。利用图注意力机制、自动交叉特征生成及频域变换，实现每周基于新数据的模型更新，并采用简单性能驱动的权重调整策略，无需频繁人工干预或固定周期重训。该方法显著提升模型稳定性和泛化能力，适用于长期信贷风险建模任务。
原文摘要 · Abstract (English)
Predicting long-term loan defaults is hard because borrower behavior often changes and data distributions shift over time. This paper presents HYDRA-EI, a hybrid ensemble incremental learning framework. It uses several stages of feature processing and combines multiple models. The framework builds relational, cross, and frequency-based features. It uses graph attention, automatic cross-feature creation, and transformations from the frequency domain. HYDRA-EI updates weekly using new data and adjusts the model weights with a simple performance-based method. It works without frequent manual changes or fixed retraining. HYDRA-EI improves model stability and generalization, which makes it useful for long-term credit risk tasks.
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