基于TabNet堆叠的信用违约预测模型的研究
Shijie Wang1,2, Xueyong Zhang1
1School of Finance, Central University of Finance and Economics, Beijing 102206, China.
Entropy (Basel, Switzerland)
|October 25, 2024
概括
本研究介绍了一种使用TabNeT-Stacking的高级信用违约预测模型. 这种新的方法比金融技术应用的传统方法提高了准确性和性能.
科学领域:
- 机器学习 机器学习
- 金融技术 金融技术
- 数据科学数据科学数据科学
背景情况:
- 传统的信用违约预测模型与金融技术的需求作斗争.
- 基于经验和单一网络的模型缺乏当前金融格局的复杂性.
研究的目的:
- 使用TabNeT-Stacking提出一个改进的信用违约预测模型.
- 提高金融技术中信用违约预测的准确性和性能.
主要方法:
- 使用PyTorch.开发了一个改进的TabNet结构.
- 通过多种群遗传算法优化特征选择,通过粒子群优化优化超参数.
- 雇员堆叠集体学习,使用改进的TabNet进行特征提取,XGBoost,LightGBM,CatBoost,KNN和SVM作为基础学习者,XGBoost作为meta-learner.
主要成果:
- 拟议的TabNeT-Stacking模型显著超过了原始模型的性能.
- 在准确性,精度,回忆,F1得分和曲线下面面积 (AUC) 中表现出卓越的性能.
结论:
- TabNeT-Stacking模型为信用违约预测提供了一个强大而有效的解决方案.
- 深度学习和整体方法的整合在金融风险评估方面取得了重大进展.
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