用贝叶斯优化驱动的策略来检测使用极端随机树的信用卡欺诈行为
Zheng You Lim1, Ying Han Pang1, Khairul Zaqwan Bin Kamarudin1
1Faculty of Information Science and Technology, Multimedia University, Ayer Keroh, Melaka 75450, Malaysia.
MethodsX
|December 6, 2024
概括
这项研究引入了一个新的AI工具,贝叶斯优化极端随机树 (TP-ERT),用于高级信用卡欺诈检测. 与现有系统相比,TP-ERT显著提高了识别欺诈交易的准确性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 信用卡使用的增加增加了欺诈风险.
- 传统的欺诈检测模型与复杂,不平衡的数据作斗争,导致过度拟合.
- 先进的机器学习对于有效的信用卡欺诈检测 (CCFD) 至关重要.
研究的目的:
- 提出一个新的贝叶斯优化极端随机树 (TP-ERT) 模型,用于增强信用卡欺诈检测.
- 改进模型的概括性和捕捉多样化的交易模式.
- 评估TP-ERT的性能与现有的CCFD系统相比.
主要方法:
- 使用极端随机树,在分割点和特征选择中增强随机性.
- 采用树结构的帕森估计器 (TPE),贝叶斯优化策略,用于超参数调整.
- 在真实世界信用卡交易数据集上评估模型性能.
主要成果:
- 拟议的TP-ERT模型在其他CCFD系统中表现出卓越的性能.
- TP-ERT获得了更高的F1分数,验证了其有效性.
- 使用TPE的贝叶斯优化证明比其他超参数调整技术更有效.
结论:
- 优化的极端随机树模型是一种可行的人工智能工具,用于检测信用卡欺诈.
- 通过树结构的Parzen Estimator进行超参数调整,通过捕获复杂的交易模式,有效地提高模型性能.
- 经验发现证实了拟议方法在现实数据上的优越性.
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