Deep adaptive ensemble learning for imbalanced credit card fraud detection

Traditional techniques for credit card fraud detection often struggle with highly imbalanced datasets, which compounds the complexity of fraudulent transaction detection. To tackle this issue, the current paper puts forth a novel technique known as deep adaptive ensemble learning (DAEL). Initially, we employ the ensemble-based under-sampling method to bring equilibrium to the data. This is followed by the application of a deep autoencoder for the purpose of feature extraction and dimensionality reduction. Then, a bidirectional long short-term memory model is trained using the features extracted from the autoencoder. Finally, an adaptive weighting technique is applied to unify multiple models. The empirical results demonstrate that the proposed DAEL technique significantly surpasses individual models and conventional ensemble learning techniques in performance.