用数据平衡+集成学习提升信用卡欺诈检测准确率
A Data Balancing and Ensemble Learning Approach for Credit Card Fraud Detection
- 结合SMOTE-KMEANS与集成模型处理数据不平衡问题
- AUC达0.96，显著提升欺诈识别的召回率与精度
- 适合金融风控系统优化，尤其关注高召回场景
本研究提出一种创新方法，通过将SMOTE-KMEANS技术与集成机器学习模型相结合，用于识别信用卡欺诈。该模型在逻辑回归、决策树、随机森林和支持向量机等传统模型上进行了性能对比，采用准确率、召回率和曲线下面积（AUC）作为评估指标。结果表明，结合SMOTE-KMEANS算法后，该模型AUC达到0.96，显著提升了对欺诈交易的检测能力，同时保持了高精确率与高召回率。研究还探讨了不同过采样技术对各类分类器性能的增强效果，验证了所提方法在平衡数据集上的鲁棒性与有效性。未来工作将聚焦于进一步优化SMOTE-KMEANS方法，并将其集成至现有欺诈检测系统中，以强化金融安全与消费者保护。
原文摘要 · Abstract (English)
This research introduces an innovative method for identifying credit card fraud by combining the SMOTE-KMEANS technique with an ensemble machine learning model. The proposed model was benchmarked against traditional models such as logistic regression, decision trees, random forests, and support vector machines. Performance was evaluated using metrics, including accuracy, recall, and area under the curve (AUC). The results demonstrated that the proposed model achieved superior performance, with an AUC of 0.96 when combined with the SMOTE-KMEANS algorithm. This indicates a significant improvement in detecting fraudulent transactions while maintaining high precision and recall. The study also explores the application of different oversampling techniques to enhance the performance of various classifiers. The findings suggest that the proposed method is robust and effective for classification tasks on balanced datasets. Future research directions include further optimization of the SMOTE-KMEANS approach and its integration into existing fraud detection systems to enhance financial security and consumer protection.
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