Missing Labels

The missing label problem in multi-label learning refers to the incomplete label information in training data, which makes it difficult to accurately capture the relationships between labels and between labels and features, thereby affecting model performance. The task aims to fill in the missing labels through reasonable methods, enhancing the robustness and accuracy of the model in practical applications, especially in large-scale label spaces where this has significant value.