COMPARATIVE EVALUATION OF MACHINE LEARNING ALGORITHMS AND CLASS IMBALANCE HANDLING STRATEGIES FOR CREDIT CARD FRAUD DETECTION
DOI:
https://doi.org/10.5281/zenodo.23319034Abstract
Severe class imbalance complicates the evaluation of credit card fraud detection models. This study compares six classifiers under no balancing, class weighting, SMOTE, and random undersampling, yielding 24 configurations. Experiments used approximately 1.85 million synthetic Sparkov transactions with a temporal train-test split. Average Precision (AP) was the primary metric, complemented by classification and alert-budget measures.Keywords
credit card fraud detection, machine learning, class imbalance, SMOTE, XGBoost, Average Precision, SHAPReferences
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