ArticleFrontiers in big data2021
A Benchmark for Data Imputation Methods.
Article in Frontiers in big data, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers.
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Who cites it
56 citing papers in PubMed.
- Benchmarking imputation methods on real-world clinical time series with simulated spatio-temporal missingness.Nature communications · 2026Article
- Identifying prospective temperament predictors of callous-unemotional traits using machine learning.European child & adolescent psychiatry · 2026Article
- A Comprehensive Survey on Online AutoML and Adversarial Robustness for IoT and EV Charging Network Security.Sensors (Basel, Switzerland) · 2026Review
- Random forest imputation and genomic prediction for missing egg production time-series data in yellow-feathered broiler breeders.BMC genomics · 2026Article
- Prediction Pipeline Selection for Incomplete Clinical Data via Missingness Fingerprints and Instance Augmentation.Bioengineering (Basel, Switzerland) · 2026Article
- Longitudinal modeling of Post-COVID-19 condition over three years: A machine learning approach using clinical, neuropsychological, and fluid markers.Scientific reports · 2026Article
- Integrating socioeconomic context with multimodal EEG data for improved ADHD risk screening.PloS one · 2026Article
- A model-agnostic framework for dataset-specific selection of missing value imputation methods in pain-related numerical data.Canadian journal of pain = Revue canadienne de la douleur · 2026Article
- Article
- Assessment of transformer-based AI in clinical oncology.BMC medical informatics and decision making · 2025Article
- A Study Protocol on Risk Prediction Modelling of Mortality and In-Hospital Major Bleeding Following Percutaneous Coronary Intervention in an Australian Population: Machine Learning Approach.Methods and protocols · 2025Article
- Self-Attention-Based Deep Learning for Missing Sensor Data Imputation in Real-Time Probe Card Monitoring.Sensors (Basel, Switzerland) · 2025Article
- Methods for Addressing Missingness in Electronic Health Record Data for Clinical Prediction Models: Comparative Evaluation.JMIR medical informatics · 2025Article
- The Oldenburg Hearing Health Record (OHHR).Scientific data · 2025Article
- RiskPath: Explainable deep learning for multistep biomedical prediction in longitudinal data.Patterns (New York, N.Y.) · 2025Article
- Machine Learning-Driven Insights in Cancer Metabolomics: From Subtyping to Biomarker Discovery and Prognostic Modeling.Metabolites · 2025Review
- Benchmarking Missing Data Imputation Methods for Time Series Using Real-World Test Cases.Proceedings of machine learning research · 2025Article
- Establishment of a machine learning-based prediction framework to assess trade-offs in decisions that affect post-HCT outcomes.Computers in biology and medicine · 2025Article
- Flexible imputation toolkit for electronic health records.Scientific reports · 2025Article
- Explainable machine learning framework for biomarker discovery by combining biological age and frailty prediction.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the increasing importance and complexity of data pipelines, data quality became one of the key challenges in modern software applications. The importance of data quality has been recognized beyond the field of data engineering and database management systems (DBMSs). Also, for machine learning (ML) applications, high data quality standards are crucial to ensure robust predictive performance and responsible usage of automated decision making. One of the most frequent data quality problems is missing values. Incomplete datasets can break data pipelines and can have a devastating impact on downstream ML applications when not detected. While statisticians and, more recently, ML researchers have introduced a variety of approaches to impute missing values, comprehensive benchmarks comparing classical and modern imputation approaches under fair and realistic conditions are underrepresented. Here, we aim to fill this gap. We conduct a comprehensive suite of experiments on a large number of datasets with heterogeneous data and realistic missingness conditions, comparing both novel deep learning approaches and classical ML imputation methods when either only test or train and test data are affected by missing data. Each imputation method is evaluated regarding the imputation quality and the impact imputation has on a downstream ML task. Our results provide valuable insights into the performance of a variety of imputation methods under realistic conditions. We hope that our results help researchers and engineers to guide their data preprocessing method selection for automated data quality improvement.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.