OverNaN extends common synthetic oversampling methods to operate directly on incomplete data vectors, preserving meaningful missingness structure during generation of minority-class samples.
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , pages =
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OverNaN: NaN-Aware Oversampling for Imbalanced Learning with Meaningful Missingness
OverNaN extends common synthetic oversampling methods to operate directly on incomplete data vectors, preserving meaningful missingness structure during generation of minority-class samples.