GMM-based Undersampling and Its Application for Credit Card Fraud Detection

• Published in: Proceedings of ... International Joint Conference on Neural Networks pp. 1 - 8
• Main Authors: Zhang, Fengjun, Liu, Guanjun, Li, Zhenchuan, Yan, Chungang, Jiang, Changjun
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2019
• Subjects: Computational modeling; Credit cards; Gaussian distribution; Machine learning algorithms; Probability density function; Testing; Training
• ISSN: 2161-4407
• DOI: 10.1109/IJCNN.2019.8852415

The class imbalance problem exists in many real-world applications such as fraud detection, medical diagnosis and spam filtering, and seriously influences the performance of learning algorithms. Randomly undersampling is a famous method to solve the problem. However, it cannot well extract the samples nearby the cross-edge of majority and minority classes due to its randomness, while these samples are very important for a classifier since they influence the classification performance. In this paper, we propose a novel Gaussian Mixture Undersampling (GMUS for short). GMUS mainly contains three steps. Firstly, a Gaussian Mixture Model (GMM) is applied to fit the majority samples. Secondly, considering the probability density function (PDF) of predicted minority samples on the well-fitted GMM, the maximum of PDF is selected as the cross-edge of two classes. Finally, we undersample the majority samples near the cross-edge. We do experiments on 16 public datasets and the results demonstrate that GMUS can sample more informative instances and thus improve the performance of classifiers compared with the state-of-the-art undersampling methods. We also apply GMUS to the credit card fraud detection and obtain a good performance.