ENHANCED MACHINE LEARNING CLASSIFICATION WITH WEIGHTED LOSS PENALTY ON CREDIT CARD FRAUD DETECTION
Abstract
This paper provides an innovative approach for data imbalance handling, namely partial penalty, to enhance the machine learning application in credit card fraud detection field.Such approach avoids the misleading dataor data missing issue brought by traditional over-sampling or under-sampling approaches, keeps the training data same as validation and testing data, and realizes a higher performance in both validation and testing scenarios.Under the partial penalty methodology, we’ve also applied five machine learning models, including Logistic Regression, Random Forest, kNN, Decision Tree, and Light Gradient Boosting, and achieves 88.35% F1 score and 98.79% AUC score in testing scenario.
Fields of Study & Subjects
Imbalanced Data Classification Techniques
Financial Distress and Bankruptcy Prediction
Artificial Intelligence in Healthcare
Publication details
- Journal / source
- Machine Learning and Applications An International Journal
- Publisher
- Academy and Industry Research Collaboration Center (AIRCC)
- Volume
- 13
- Issue
- 1
- Pages
- 09–20
- Publication date
- 28 March 2026