Credit Card Fraud Detection using Feature Augmentation based Boosted Ensemble (FABE)
DOI:
https://doi.org/10.26438/ijcse/v6i12.841846Keywords:
Credit card fraud detection, Ensemble model, Feature Augmentation, Feature Reduction, Feature Engineering, BoostingAbstract
Fraud detection in credit card transactions have become mandatory for the financial services industry due to the huge levels of automations observed in the industry. This work presents a Feature Augmentation based Boosted Ensemble (FABE) for credit card fraud detection on huge data. The proposed model integrates two major components; feature augmentation and ensemble creation. Feature augmentation phase performs feature reduction, feature transformation and feature engineering. Feature reduction aids in effective elimination of unnecessary features, while feature transformation and feature engineering aids in creation of new features that can aid in better predictions. The ensemble creation phase models a boosted ensemble using Decision Trees. Multiple training data bags are created, and multiple base learners are created. The learner with highest weight and lowest error levels is iteratively modelled and used as the final learner. Experiments were performed and comparisons with existing models in literature exhibit the high-performance levels of the proposed FABE model.
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