A Hybrid Approach to Credit Card Fraud Detection: Integrating Adaboost and Majority Voting for Enhanced Accuracy and Robustness
DOI:
https://doi.org/10.70162/fcr/2024/v2/i1/v2i1s07Keywords:
Credit Card Fraud, Adaboost, Majority Voting, Ensemble Learning, Imbalanced Datasets, Fraud DetectionAbstract
Fraud detection in credit card transactions is a critical challenge in the financial sector. This paper presents a hybrid approach utilizing Adaboost and Majority Voting algorithms to enhance the accuracy and robustness of fraud detection systems. Using the publicly available "Credit Card Fraud Detection Dataset" from the ULB Machine Learning Group, containing 284,807 transactions with only 492 fraudulent cases (0.172%), this study tackles the challenges of class imbalance and evolving fraud tactics. The hybrid model achieves a significant improvement in performance metrics, including an F1-score of 82.3% and an AUC of 0.95, surpassing standalone classifiers and individual ensemble methods. These results demonstrate the potential of the hybrid approach for real-world implementation in financial systems requiring high accuracy and reliability.
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