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A flexible class of dependence-aware multi-label loss functions
2022
Machine Learning
AbstractThe idea to exploit label dependencies for better prediction is at the core of methods for multi-label classification (MLC), and performance improvements are normally explained in this way. Surprisingly, however, there is no established methodology that allows to analyze the dependence-awareness of MLC algorithms. With that goal in mind, we introduce a class of loss functions that are able to capture the important aspect of label dependence. To this end, we leverage the mathematical
doi:10.1007/s10994-021-06107-2
fatcat:rdt7rez2bvhbxlkzrxrwvdxpqm