Tail Label

Tail label prediction focuses on improving the accuracy of machine learning models in classifying less frequent data points, a common challenge in multi-label classification problems with long-tailed distributions. Current research emphasizes developing novel loss functions (like variations of CVaR), leveraging graph structures and external knowledge bases to augment scarce data, and employing advanced architectures such as transformers and ensemble methods to enhance model performance, particularly for these under-represented labels. Addressing this challenge is crucial for improving the reliability and fairness of machine learning systems across diverse applications, including e-commerce, healthcare, and information retrieval, where accurate prediction of rare events is vital.