Multi-class Ensemble Learning of Imbalanced Bidding Fraud Data

摘要

E-auctions are vulnerable to Shill Bidding (SB), the toughest fraud to detect due to its resemblance to usual bidding behavior. To avoid financial losses for genuine buyers, we develop a SB detection model based on multi-class ensemble learning. For our study, we utilize a real SB dataset but since the data are unlabeled, we combine a robust data clustering technique and a labeling approach to categorize the training data into three classes. To solve the issue of imbalanced SB data, we use an advanced multi-class over-sampling method. Lastly, we compare the predictive performance of ensemble classifiers trained with balanced and imbalanced SB data. Combining data sampling with ensemble learning improved the classifier accuracy, which is significant in fraud detection problems.