Researchers have developed UniPROT, a framework that uses partial optimal transport to select prototypes uniformly from source data distributions, improving representation of minority classes in imbalanced datasets without sacrificing majority class accuracy. This method offers a scalable and theoretically sound solution for prototype selection, benefiting developers working on machine learning models dealing with skewed data distributions.
Read the full article at arXiv cs.LG (ML)
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alinemati1983-6987Written by alinemati1983-6987
