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Class-Weighted Classification: Trade-offs and Robust Approaches
[article]
2020
arXiv
pre-print
We address imbalanced classification, the problem in which a label may have low marginal probability relative to other labels, by weighting losses according to the correct class. First, we examine the convergence rates of the expected excess weighted risk of plug-in classifiers where the weighting for the plug-in classifier and the risk may be different. This leads to irreducible errors that do not converge to the weighted Bayes risk, which motivates our consideration of robust risks. We define
arXiv:2005.12914v1
fatcat:2keuzsj5djei7lyss2kwe4cl3i