Comparative analysis of binary and one-class classification techniques for credit card fraud data
Joffrey L. Leevy, John Hancock, Taghi M. Khoshgoftaar
Abstract
Abstract The yearly increase in incidents of credit card fraud can be attributed to the rapid growth of e-commerce. To address this issue, effective fraud detection methods are essential. Our research focuses on the Credit Card Fraud Detection Dataset, which is a widely used dataset that contains real-world transaction data and is characterized by high class imbalance. This dataset has the potential to serve as a benchmark for credit card fraud detection. Our work evaluates the effectiveness of two supervised learning classification techniques, binary classification and one-class classification, for credit card fraud detection. The performance of five binary-class classification (BCC) learners and three one-class classification (OCC) learners is evaluated. The metrics used are area under the precision-recall curve (AUPRC) and area under the receiver operating characteristic curve (AUC). Our results indicate that binary classification is a better approach for detecting credit card fraud than one-class classification, with the top binary classifier being CatBoost.