对比四类模型在欺诈检测中的表现，选型需权衡准确率与可解释性。
Evaluating Supervised Learning Models for Fraud Detection: A Comparative Study of Classical and Deep Architectures on Imbalanced Transaction Data
- 用逻辑回归、随机森林、LightGBM和GRU对比分类效果
- 轻量梯度提升机在整体和类别指标上最优
- GRU召回高但精度低，适合容忍误报的场景
欺诈检测在金融与电商等高风险领域至关重要，未识别的欺诈交易可能导致重大经济损失。本研究系统比较了四种监督学习模型——逻辑回归、随机森林、轻量梯度提升机（LightGBM）和门控循环单元（GRU）网络——在大规模、高度不平衡的在线交易数据集上的表现。集成方法如随机森林和LightGBM在总体及各类别指标上均表现更优；逻辑回归则提供了可靠且可解释的基线。GRU模型对少数类欺诈样本具有较强的召回能力，但以牺牲精度为代价，凸显实际部署中的权衡问题。评估不仅关注加权平均指标，还深入分析每类的精确率、召回率和F1分数，全面揭示各模型在检测罕见但关键欺诈行为时的有效性。研究强调，模型选择应根据欺诈检测系统的具体风险容忍度和运营需求而定。
原文摘要 · Abstract (English)
Fraud detection remains a critical task in high-stakes domains such as finance and e-commerce, where undetected fraudulent transactions can lead to significant economic losses. In this study, we systematically compare the performance of four supervised learning models - Logistic Regression, Random Forest, Light Gradient Boosting Machine (LightGBM), and a Gated Recurrent Unit (GRU) network - on a large-scale, highly imbalanced online transaction dataset. While ensemble methods such as Random Forest and LightGBM demonstrated superior performance in both overall and class-specific metrics, Logistic Regression offered a reliable and interpretable baseline. The GRU model showed strong recall for the minority fraud class, though at the cost of precision, highlighting a trade-off relevant for real-world deployment. Our evaluation emphasizes not only weighted averages but also per-class precision, recall, and F1-scores, providing a nuanced view of each model's effectiveness in detecting rare but consequential fraudulent activity. The findings underscore the importance of choosing models based on the specific risk tolerance and operational needs of fraud detection systems.
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