A Hybrid Grey Wolf - Meta Heuristic Optimization and Random Forest Classifier for Handling Imbalanced Credit Card Fraud Data
Keywords:
Credit Card fraud, Grey Wolf meta heuristic, random Forest, SMOTE ENNAbstract
Because of COVID 19 and the development of information technology, individuals prefer to frequently purchase online for necessities and pay with credit cards. In these online digital transactions, credit card fraud is one of the main problems that result in financial loss for customers. The identification of such online credit card fraud has been the subject of numerous studies. To automate this process of detecting credit card fraud, a number of machine learning and data mining approaches have been developed. This study presents a Hybrid Grey Wolf optimization approach and Random Forest classifier (HGWRF) with three sequence levels for detecting credit card fraud. In the first level, a credit card data set is collected and balanced using a combined SMOTE ENN sampling technique. Grey wolf meta heuristic approach is used in the second level to optimize the subset of features. The Random Forest machine learning classifier is employed in the third level to learn the model for the credit card fraudulent detection system. It assesses performance using basic metrics and MCC, CV score, R2 score, MSE, kappa score. The suggested HGWRF improves accuracy by 0.87 to 0.946 and outperforms well when compared with other non-optimization machine learning algorithms.
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