Balanced Distribution

Balanced distribution in machine learning focuses on mitigating the negative impacts of imbalanced datasets, where some classes are significantly under-represented compared to others. Current research emphasizes techniques like data augmentation, ensemble methods (e.g., using generative models), and novel loss functions to achieve more balanced class distributions during training, improving model robustness and fairness. This work is crucial for enhancing the reliability and generalizability of machine learning models across diverse applications, particularly in scenarios with naturally skewed data distributions, such as object detection and customer lifetime value prediction.