Classification of imbalanced auction fraud data

Abstract

Online auctioning has attracted serious fraud given the huge amount of money involved and anonymity of users. In the auction fraud detection domain, the class imbalance, which means less fraud instances are present in bidding transactions, negatively impacts the classification performance because the latter is biased towards the majority class i.e. normal bidding behavior. The best-designed approach to handle the imbalanced learning problem is data sampling that was found to improve the classification efficiency. In this study, we utilize a hybrid method of data over-sampling and under-sampling to be more effective in addressing the issue of highly imbalanced auction fraud datasets. We deploy a set of well-known binary classifiers to understand how the class imbalance affects the classification results. We choose the most relevant performance metrics to deal with both imbalanced data and fraud bidding data.