ml-lab-sau/Discriminatory-Label-specific-Weights-for-Multi-label-Learning-with-Missing-Labels

To deal with the class imbalance problem in multi-label learning with missing labels, we propose Class Imbalance aware Missing labels Multi-label Learning, CIMML. Our proposed method handles class imbalance issue by constructing a label weight matrix with weight estimation guided by how frequently a label is present, absent, and unobserved.

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