Evaluating the Impact of Majority versus Minority Class Training for One-Class Classification

In this research, we assess the impact of training One-Class Classification (OCC) algorithms on the majority class versus the minority class, by employing highly imbalanced large and big datasets. It is worth highlighting that the accessibility of class data can be a major hurdle in training models. Depending on the situation, it may be possible to obtain one class within a reasonable time frame while not obtaining another. Our study centers around detecting fraudulent activities using the Credit Fraud Detection Dataset and a dataset derived from Medicare Part B, Part D, and the Durable Medical Equipment, Prosthetics, Orthotics and Supplies (DMEPOS) data, combined with the labels of the List of Excluded Individuals and Entities (LEIE) records. The Credit Card Fraud Detection Dataset, with its real-world transactional data and pronounced class disparity, serves as a benchmark for fraud detection. Notably, it stands as the only large-scale public dataset tailored for credit card fraud analysis. In Part B, Part D, and DMEPOS, researchers can investigate national trends and patterns based on Medicare big data. For our experiments, we use One-Class Gaussian Mixture Model (GMM), One-Class Adversarial Nets (OCAN), and One-Class Support Vector Machine (SVM). Their classification performance is evaluated with the Area Under the Precision-Recall Curve (AUPRC) and Area Under the Receiver Operating Characteristic Curve (AUC). Given the challenges posed by imbalanced datasets, our results indicate that strategically focusing training on the majority class yields better results.