An Ensemble Learning Framework for Credit Card Fraud Detection Based on Training Set Partitioning and Clustering
Hongyu Wang, Ping Zhu, Xueqiang Zou, Su‐Juan Qin · 2018
The popularity of credit card has greatly facilitated the transactions between merchants and cardholders. However, credit card fraud has been derived, which results in losses of billions of euros every year. In recent years, machine learning and data mining technology have been widely used in fraud detection and achieved favorable performances. Most of these studies use the technology of under-sampling to deal with the high imbalance of credit card data. However, it will potentially discard some relevant training samples which will weaken the ability of the classifier. In this paper, we propose an ensemble learning framework based on training set partitioning and clustering. It turns out that the proposed framework not only ensures the integrity of the sample features, but also solves the high imbalance of the dataset. A main feature of our framework is that every base estimator can be trained in parallel. This improves the efficiency of the framework. We show the effectiveness of our proposed ensemble framework by experimental results on a real credit card transaction dataset.