用LDA降维提升信用评分模型准确率与可解释性
Linear Discriminant Analysis in Credit Scoring: A Transparent Hybrid Model Approach
- 结合LDA降维与XGBoost+DNN构建混合模型
- 准确率达99.45%，F1分数达99%
- 支持局部（LIME）和全局（敏感性分析）解释
计算技术的发展使机器学习（ML）和深度学习（DL）在信用评分中得以应用。尽管复杂模型预测更准，但可解释性下降，而信用评分重视决策公平性。我们采用线性判别分析（LDA）作为特征压缩方法，降低模型复杂度。对比了6种机器学习模型、1个深度学习模型及带/不带LDA的混合模型。结果表明，基于LDA的混合模型XG-DNN表现最优，准确率为99.45%，F1分数为99%。此外，通过LIME（局部）和Morris敏感性分析（全局）实现模型决策解释。研究证明，特征压缩可在不损失性能与可解释性的前提下，显著降低计算负担，适用于资源受限场景。
原文摘要 · Abstract (English)
The development of computing has made credit scoring approaches possible, with various machine learning (ML) and deep learning (DL) techniques becoming more and more valuable. While complex models yield more accurate predictions, their interpretability is often weakened, which is a concern for credit scoring that places importance on decision fairness. As features of the dataset are a crucial factor for the credit scoring system, we implement Linear Discriminant Analysis (LDA) as a feature reduction technique, which reduces the burden of the models complexity. We compared 6 different machine learning models, 1 deep learning model, and a hybrid model with and without using LDA. From the result, we have found our hybrid model, XG-DNN, outperformed other models with the highest accuracy of 99.45% and a 99% F1 score with LDA. Lastly, to interpret model decisions, we have applied 2 different explainable AI techniques named LIME (local) and Morris Sensitivity Analysis (global). Through this research, we showed how feature reduction techniques can be used without affecting the performance and explainability of the model, which can be very useful in resource-constrained settings to optimize the computational workload.
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