Class Wise Multiplier

Class-wise multipliers are parameters used to adjust the contribution of individual classes within machine learning models, particularly in addressing class imbalance or improving calibration. Research focuses on incorporating these multipliers into optimization algorithms like the Alternating Direction Method of Multipliers (ADMM) and employing them within various architectures, including deep neural networks and recurrent neural networks, to enhance model performance and efficiency. This approach offers significant potential for improving the accuracy and robustness of machine learning models across diverse applications, particularly in scenarios with skewed class distributions or where specific classes require more careful weighting.