mlsanityData to 14 Oct 2026

Classification of datasets with imputed missing values: does imputation quality matter?

arXiv 2206.08478 ↗First posted 16 Jun 2022cs.LGcited by 22

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Abstract (arXiv)

Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in most real-world datasets and these missing values are typically imputed using established methods, followed by classification of the now complete, imputed, samples. The focus of the machine learning researcher is then to optimise the downstream classification performance. In this study, we highlight that it is imperative to consider the quality of the imputation. We demonstrate how the commonly used measures for assessing quality are flawed and propose a new class of discrepancy scores which focus on how well the method recreates the overall distribution of the data. To conclude, we highlight the compromised interpretability of classifier models trained using poorly imputed data.

Key facts. Cited by 22 papers in the map (data through 14 Oct 2026). Builds on 12 of its 85 references that are in the map. First posted on arXiv 16 Jun 2022.

Builds on

12 of 85 references.

References · 12

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
  2. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  3. 2015Importance Weighted Autoencoderscited by 456
  4. 2014New insights and perspectives on the natural gradient methodcited by 443
  5. 2018GAIN: Missing Data Imputation using Generative Adversarial Netscited by 352
  6. 2020Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scanscited by 158
  7. 2019MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-IIIcited by 122
  8. 2020Missing Data Imputation using Optimal Transportcited by 100
  9. 2019NGBoost: Natural Gradient Boosting for Probabilistic Predictioncited by 94
  10. 2015An Empirical Comparison of Multiple Imputation Methods for Categorical Datacited by 8
  11. 2021Are deep learning models superior for missing data imputation in large surveys? Evidence from an empirical comparisoncited by 7
  12. 2020Missing Data Imputation for Classification Problemscited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 2; 2024: 6; 2025: 6; 2026: 7.

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  1. 2022Diffusion-based Time Series Imputation and Forecasting with Structured State Space Modelscited by 198
  2. 2023REFORMS: Reporting Standards for Machine Learning Based Sciencecited by 13
  3. 2024Imputation for prediction: beware of diminishing returnscited by 13
  4. 2023Recent Methodological Advances in Federated Learning for Healthcarecited by 11
  5. 2024Interpretable Generalized Additive Models for Datasets with Missing Valuescited by 7
  6. 2024Mining of Switching Sparse Networks for Missing Value Imputation in Multivariate Time Seriescited by 5
  7. 2025Leveraging Predictive Equivalence in Decision Treescited by 4
  8. 2024Risk-based Calibration for Generative Classifierscited by 2
  9. 2024Still More Shades of Null: An Evaluation Suite for Responsible Missing Value Imputationcited by 1
  10. 2026Scalable On-Hardware Training of Quantum Neural Networks and Application to Clinical Data Imputationcited by 1
  11. 2024HyperMM : Robust Multimodal Learning with Varying-sized Inputscited by 0
  12. 2025To impute or not to impute: How machine learning modelers treat missing datacited by 0
10 more
  1. 2025Prediction Models That Learn to Avoid Missing Valuescited by 0
  2. 2025Decentralized Federated Learning of Probabilistic Generative Classifierscited by 0
  3. 2025No Modality Left Behind: Dynamic Model Generation for Incomplete Medical Datacited by 0
  4. 2025Imputation Uncertainty in Interpretable Machine Learning Methodscited by 0
  5. 2026Drop the mask! GAMM-A Taxonomy for Graph Attributes Missing Mechanismscited by 0
  6. 2026Characterizing the Predictive Impact of Modalities with Supervised Latent-Variable Modelingcited by 0
  7. 2026tBayes-MICE: A Bayesian Approach to Multiple Imputation for Time Series Datacited by 0
  8. 2026Predicting missing values: A good idea?cited by 0
  9. 2026Novel Dynamic Batch-Sensitive Adam Optimiser for Vehicular Accident Injury Severity Predictioncited by 0
  10. 2026When Does Task-Aware Imputation Matter? An Analysis of Robustness Under Train-Test Mismatchcited by 0