OverNaN: NaN-Aware Oversampling for Imbalanced Learning with Meaningful Missingness

TL;DR

OverNaN introduces a NaN-aware oversampling method that preserves meaningful missingness in imbalanced datasets, enhancing data augmentation without losing valuable information.

Contribution

It extends synthetic oversampling techniques to operate directly on incomplete data, maintaining missingness structure rather than imputing or deleting it.

Findings

OverNaN effectively retains missingness during oversampling.

It improves class balance without distorting missing data patterns.

The method is suitable for small, incomplete datasets in scientific domains.

Abstract

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, introduce bias, and reduce generalisability; particularly in imbalanced datasets where minority classes are already under-represented. OverNaN is a lightweight, NaN-aware oversampling framework designed to address class imbalance without erasing missingness structure. It extends common synthetic oversampling methods to operate directly on incomplete feature vectors, allowing missing values to be preserved, propagated, or selectively interpolated according to explicitly defined strategies. Rather than repairing missing data, OverNaN treats missingness as part of the feature space over which synthetic samples are generated. This paper situates OverNaN within the broader landscape of imbalanced learning, missing-data handling, and NaN-tolerant algorithms. Using representative examples included with the software, we demonstrate that meaningful missingness can be retained during oversampling without introducing artificial certainty. OverNaN is intended for practitioners working with small, incomplete, and imbalanced datasets in scientific and engineering domains where missingness is unavoidable and often informative.