Class imbalance in out-of-distribution datasets: Improving the robustness of the TextCNN for the classification of rare cancer types

•Class imbalance and class distribution degrade model performance at deployment time.•Class-specialized ensembles improve classification performance of rare cancer types.•Performance gain obtained with traditional ensembles comes from the majority classes.•Test accuracy improvements gained from two-phase learning can be misleading.