AI RESEARCH
OverNaN: NaN-Aware Oversampling for Imbalanced Learning with Meaningful Missingness
arXiv CS.LG
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ArXi:2605.11525v1 Announce Type: new Missing values are routinely treated as defects to be eliminated through deletion or imputation prior to machine learning. In many applied domains, however, missingness itself carries information, reflecting experimental constraints, measurement choices, or systematic mechanisms tied to the data-generating process. Eliminating or masking this structure can distort class boundaries,