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Imbalanced class distribution is an inherent problem in many real-world classification tasks where the minority class is the class of interest. Many conventional statistical and machine learning classification algorithms are subject to frequency bias and learning discriminating boundaries between the minority and majority classes could be challenging. To address the class distribution imbalance in deep learning, we propose a class re-balancing strategy based on a class-balanced dynamically weighted loss function where weights are assigned based on class frequency and predicted probability of ground truth class. The ability of dynamic weighting scheme to self-adapt its weights depending on the prediction scores allows the model to adjust for instances with varying levels of difficulty resulting in gradient updates driven by hard minority class samples. We further show that the proposed loss function is classification calibrated. Experiments conducted on highly imbalanced data across different applications of cyber intrusion detection (CICIDS2017 dataset) and medical imaging (ISIC2019 dataset) show robust generalization. Theoretical results supported by superior empirical performance provide justification for the validity of the proposed Dynamically Weighted Balanced (DWB) Loss Function.

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