Tackling Class Imbalance Problem with Adversarial Attention-based Variational Graph Autoencoder: Study in Fraud Detection

Nur Alibasyah Wiriaatmadja; Finny Oktariani

Nur Alibasyah Wiriaatmadja Department of Computational Science, Institut Teknologi Bandung, Jalan Ganesa 10, Bandung 40132, Indonesia
Finny Oktariani Combinatorial Mathematics Research Group, FMIPA, Institut Teknologi Bandung, Jalan Ganesha 10, Bandung 40132, Indonesia

Keywords: imbalance classification, graph embedding, graph autoencoder, graph neural network, fraud detection

Abstract

Class Imbalance is often encountered in many classification problems for machine learning, resulting in bias towards the majority class. Various techniques have been developed to address this issue, focusing on oversampling the minority class or undersampling the majority class. This research aims to tackle the class imbalance problem with a different approach by using Adversarial Attention-based Variational Graph Autoencoder (AAVGA) introduced by Weng et al. in 2020. This approach is studied for fraud detection, which graph model of classification task are first constructed by mapping each entity as nodes and transaction between them as edges. The experiment is conducted by varying class distributions to analyze how imbalance class influences the prediction score. We obtained that our approach produced better precision and recall score, even for extremely imbalanced dataset.

References

He, Haibo, and Edwardo A. Garcia. "Learning from imbalanced data." IEEE Transactions on knowledge and data engineering 21.9 (2009): 1263-1284.

Wei, Wei, et al. "Effective detection of sophisticated online banking fraud on extremely imbalanced data." World Wide Web 16 (2013): 449-475.

Makki, Sara, et al. "An experimental study with imbalanced classification approaches for credit card fraud detection." IEEE Access 7 (2019): 93010-93022.

Wang, Daixin, et al. "A semi-supervised graph attentive network for financial fraud detection." 2019 IEEE International Conference on Data Mining (ICDM). IEEE, 2019.

Weng, Ziqiang, Weiyu Zhang, dan Wei Dou. "Adversarial attention-based variational graph autoencoder." IEEE Access 8 (2020): 152637-152645.

Lopez-Rojas, Edgar Alonso, & Stefan Axelsson. "Banksim: A bank payments simulator for fraud detection research." 26th European Modeling and Simulation Symposium, EMSS. 2014.

Wiriaatmadja, N. A., & Oktariani, F. (2023). “Adversarial Attention-Based Variational Graph Autoencoder for Fraud Detection in Online Financial Transaction”. [Manuscript in preparation].