一种混合的信用卡欺诈检测模型

A Hybrid Credit Card Fraud Detection Model

信用卡欺诈检测是一个重要的问题,为了提升对于真实世界的信用卡欺诈数据的识别率,提出了一种混合的信用卡欺诈检测模型AWFD(Anomaly weight of credit card fraud detection),首先通过异常检测的方法将数据划分为可信和异常数据,然后利用半监督的方法训练一个集成模型,最终再利用异常检测进一步剔除检测结果中的异常结果。AWFD在保障对于可信数据的学习效果上,通过半监督集成学习的方法,利用异常数据进一步扩充集成模型的多样性,并将异常检测和集成模型融合。实验结果表明,比起一些传统的机器学习方法,AWFD可以提高整体的信用卡欺诈检测的识别率。

Credit card fraud detection is a serious problem.In order to improve the recognition rate of real-world credit card fraud data,a hybrid credit card fraud detection model AWFD(Anomaly weight of credit card fraud detection)is proposed.Firstly,the da⁃ta is divided into trusted and abnormal data by anomaly detection method,and then an ensemble model is trained by semi-super⁃vised method.Finally,anomaly detection is used to further eliminate the abnormal results in the detection results.On the basis of guaranteeing the learning effect of trusted data,AWFD uses the abnormal data to further expand the diversity of the ensemble mod⁃el by semi-supervised ensemble learning method,and integrates the anomaly detection and the ensemble model.The experimental results show that AWFD can improve the overall recognition rate of credit card fraud detection compared with some traditional ma⁃chine learning methods.