Department of Electronic and Electrical Engineering, Brunel University London, Uxbridge, UB8 3PH, United Kingdom.
Visiting Professor, School of Electronic and Information Engineering, Tongji University, Shanghai, China.
PLoS One. 2022 Jan 20;17(1):e0260579. doi: 10.1371/journal.pone.0260579. eCollection 2022.
With the advancement in machine learning, researchers continue to devise and implement effective intelligent methods for fraud detection in the financial sector. Indeed, credit card fraud leads to billions of dollars in losses for merchants every year. In this paper, a multi-classifier framework is designed to address the challenges of credit card fraud detections. An ensemble model with multiple machine learning classification algorithms is designed, in which the Behavior-Knowledge Space (BKS) is leveraged to combine the predictions from multiple classifiers. To ascertain the effectiveness of the developed ensemble model, publicly available data sets as well as real financial records are employed for performance evaluations. Through statistical tests, the results positively indicate the effectiveness of the developed model as compared with the commonly used majority voting method for combination of predictions from multiple classifiers in tackling noisy data classification as well as credit card fraud detection problems.
随着机器学习的进步，研究人员继续设计和实施有效的智能方法，以在金融领域检测欺诈行为。事实上，信用卡欺诈每年给商家造成数十亿美元的损失。在本文中，设计了一个多分类器框架来解决信用卡欺诈检测的挑战。设计了一个具有多个机器学习分类算法的集成模型，其中利用行为知识空间（BKS）来结合多个分类器的预测。为了确定所开发的集成模型的有效性，使用了公开可用的数据集合和真实财务记录来进行性能评估。通过统计测试，结果积极表明，与常用的多数投票方法相比，所开发的模型在处理噪声数据分类和信用卡欺诈检测问题方面，在处理多个分类器的预测组合方面更加有效。
