A novel hybrid sampling method based on CWGAN for extremely imbalanced backorder prediction

Product backorder is a common problem in supply chain management systems. It is essential for entrepreneurs to predict the likelihood of backorder accurately to minimize a company’s losses. However, existing methods are hard to achieve satisfactory results since the number of backorders and non-backorders are extremely imbalanced. Besides, the backorder data’s attributes are complex to oversample them effectively. To address these problems, a novel hybrid sampling method is proposed to help predict extremely imbalanced backorder. The Randomized Undersampling (RUS) and a Conditional Wasserstein Generative Adversarial Network (CWGAN) are innovatively introduced into backorder prediction. First, RUS is used to reduce the majority non-backorder samples. Second, CWGAN is served as an oversampling technique to generate high-quality backorder samples. It utilizes unique structures in the generator and the discriminator to effectively model both numerical and categorical variables. Finally, the training dataset is balanced, and the Random Forest Classifier (RFC) is adopted to make backordering prediction. In the experiments of Kaggle’s dataset ‘Can you predict product backorder?’, our proposed method is superior to all benchmark methods in terms of standard evaluation metrics. The results show that our proposed product backorder prediction model is effective.