CTCN:基于条件表式生成对抗网络和时间卷积网络的新型信用卡欺诈检测方法
1School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai, China.
PeerJ. Computer science
|October 23, 2023
概括
本研究介绍了CTCN,这是一种使用条件表式生成对抗网络 (CTGAN) 进行数据平衡和时间卷积网络 (TCN) 进行序列分析的新型信用卡欺诈检测方法,提高了检测准确度.
科学领域:
- 计算机科学 计算机科学
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 信用卡欺诈对个人和机构构成重大财务风险.
- 现有的欺诈检测方法与不平衡的数据集和复杂的交易模式作斗争.
研究的目的:
- 为了提出和评估一种新的信用卡欺诈检测方法,CTCN.
- 通过解决数据不平衡和时间依赖,提高欺诈检测的准确性.
主要方法:
- 使用条件表式生成对抗网络 (CTGAN) 进行过量采样和数据平衡.
- 实施了邻里清洁规则 (NCL),通过删除重叠的多数类样本来完善数据集.
- 使用时间卷积网络 (TCN) 来分析交易序列并捕获长期依赖关系.
主要成果:
- CTCN有效地生成了合成少数群体类样本,创建了一个平衡的数据集.
- 为了更好地检测欺诈行为,TCN成功地识别了交易序列中的关系.
- 三个公共数据集的实验结果显示,CTCN的性能优于现有的机器和深度学习方法.
结论:
- 拟议的CTCN方法为检测信用卡欺诈提供了一个强大的解决方案.
- 与当前最先进的技术相比,CTCN在召回,F1-Score和AUC-ROC方面表现优越.
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