  Classification on imbalanced datasets is a challenging task in real-world
applications. Training conventional classification algorithms directly by
minimizing classification error in this scenario can compromise model
performance for minority class while optimizing performance for majority class.
Traditional approaches to the imbalance problem include re-sampling and
cost-sensitive methods. In this paper, we propose a neural network model with
novel loss function, CRCEN, for imbalanced classification. Based on the
weighted version of cross entropy loss, we provide a theoretical relation for
model predicted probability, imbalance ratio and the weighting mechanism. To
demonstrate the effectiveness of our proposed model, CRCEN is tested on several
benchmark datasets and compared with baseline models.
