A Comparison of Data Balancing Techniques for Credit Card Fraud Detection using Neural Network
Atul Kumar Uttam, Gaurav Sharma · 2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) · 2021
This research study has employed an unequalled credit card dataset and evaluated several approaches of data balancing by using a basic neural network-based model with only one hidden layer of size 32. Even with a simplified design, the supervised machine-learning model has produced optimal results in contrast to sample-based approaches for the over-sampling of data balance. The proposed model based on random over-sampling performs better than other two model based on over-sampling approaches (SMOTE, SMOTETomek).