对比五种模型在信用卡欺诈检测中的表现，发现混合方法提升检测效果。
Credit Card Fraud Detection
- 采用混合采样策略处理数据不平衡问题
- 混合方法使MLP和KNN的召回率与精确率平衡最佳
- 适合关注真实场景下欺诈检测性能的研究者
信用卡欺诈仍是重大挑战，因类别不平衡且欺诈行为模仿合法操作。本研究在真实世界数据集上评估了五种机器学习模型——逻辑回归、随机森林、XGBoost、K近邻（KNN）和多层感知机（MLP），使用欠采样、SMOTE及混合方法进行处理。模型在原始不平衡测试集上评估，以更真实反映实际性能。结果表明，混合方法在召回率与精确率之间取得最佳平衡，尤其显著提升了MLP和KNN的表现。
原文摘要 · Abstract (English)
Credit card fraud remains a significant challenge due to class imbalance and fraudsters mimicking legitimate behavior. This study evaluates five machine learning models - Logistic Regression, Random Forest, XGBoost, K-Nearest Neighbors (KNN), and Multi-Layer Perceptron (MLP) on a real-world dataset using undersampling, SMOTE, and a hybrid approach. Our models are evaluated on the original imbalanced test set to better reflect real-world performance. Results show that the hybrid method achieves the best balance between recall and precision, especially improving MLP and KNN performance.
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