Context-aware Data Sampling with Reciprocal Nearest Neighbors for Fraud Classification

In this paper, we present Modified ADASYN (M-ADASYN), an oversampling technique designed to address class imbalance in fine-grained fraud classification tasks. Unlike traditional oversampling methods that often generate noisy synthetic samples near decision boundaries, M-ADASYN incorporates Reciprocal Nearest Neighbors (RNN) to focus on densely populated minority regions, minimizing overlap with majority class data. This approach improves the quality of synthetic samples, reduces false positives, and enhances model generalization. Extensive experimental evaluation demonstrates that M-ADASYN outperforms methods such as SMOTE, ADASYN, and GAN-based approaches, by an average of 3 percentage points improvement in classification accuracy. Furthermore, M-ADASYN is shown to reduce training time while maintaining model performance across multiple classification models, including Random Forest and LightGBM. The proposed method’s integration of RNN ensures more efficient handling of extreme class imbalance, leading to improved accuracy and computational efficiency.