Partial Label Learning

Partial Label Learning is a machine learning approach designed to handle situations where each training sample is associated with multiple candidate labels, but only one of them is the true label. Its objective is to identify and leverage this partial label information through algorithms to enhance the predictive accuracy and generalization capability of the model. This method holds significant application value in multi-label classification problems, especially in scenarios where the cost of obtaining labels is high or there is uncertainty, as it can effectively improve data utilization and model performance.