The DOI matches its Crossref record; 43 of the 45 references it cites were found in a registry or an index.
Comparison of Performance of Data Imputation Methods for Numeric Dataset
Open accessAbstract
Missing data is common problem faced by researchers and data scientists. Therefore, it is required to handle them appropriately in order to get better and accurate results of data analysis. Objective of this research paper is to provide better understanding of data missingness mechanism, data imputation methods, and to assess performance of the widely used data imputation methods for numeric dataset. It will help practitioners and data scientists to select appropriate method of data imputation for numeric dataset while performing data mining task. In this paper, we comprehensively compare seven data imputation methods namely mean imputation, median imputation, kNN imputation, predictive mean matching, Bayesian Linear Regression (norm), Linear Regression, non-Bayesian (norm.nob), and random sample. We have used five different numeric datasets obtained from UCI machine learning repository for analyzing and comparing performance of the data imputation methods. Performance of the data imputation methods is assessed using Normalized Root Mean Square Error (RMSE) method. The results of analysis show that kNN imputation method outperforms the other methods. It has also been found that performance of the data imputation method is independent of the dataset and percentage of missing values in the dataset.
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“Mean imputation was applied to manage data missing completely at random.62,63”
From Mindfulness and Psychosocial Symptoms in People with Cancer: Testing Rumination and Experiential Avoidance as Mediators, and Sex as a Moderator. (2025) · cites this paper as [62]
Experimental — accuracy not yet measuredIn the citing paper
“KNN imputation has been shown to work well for numerical imputation on single datasets (Jadhav et al., 2019), but the algorithm is sensitive to the choice of the number of nearest neighbors (k) and is limited in that it does not utilize the potential structure across the entire set of observations.”
From Missing data in multi-omics integration: Recent advances through artificial intelligence (2023) · cites this paper as Jadhav et al., 2019
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Citations
Cites
Multiple Imputation for Nonresponse in Surveys
Rubin, D. B. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316696 · Cited by 20,701
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mice: Multivariate Imputation by Chained Equations in R
Buuren, S. V., & Groothuis-Oudshoorn, K. (2011) · Journal of Statistical Software · doi:10.18637/jss.v045.i03 · Cited by 13,870
Missing data: our view of the state of the art
Schafer, J. L., & Graham, J. W. (2002) · Psychological Methods · doi:10.1037/1082-989x.7.2.147 · Cited by 11,638
RUBIN, D. B. (1976) · Biometrika · doi:10.1093/biomet/63.3.581 · Cited by 9,925
Multiple imputation using chained equations: Issues and guidance for practice
White, I. R., Royston, P., & Wood, A. M. (2011) · Statistics in Medicine · doi:10.1002/sim.4067 · Cited by 9,792
Statistical Analysis with Missing Data
Little, R. J. A., & Rubin, D. B. (2002) · Wiley Series in Probability and Statistics · doi:10.1002/9781119013563 · Cited by 8,473
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Missing data analysis: making it work in the real world
Graham, J. W. (2009) · Annual Review of Psychology · doi:10.1146/annurev.psych.58.110405.085530 · Cited by 6,163
Analysis of Incomplete Multivariate Data
Schafer, J. L. (1997) · doi:10.1201/9781439821862 · Cited by 6,012 · cited 1×
“Much has been published in the statistical literature on missing data (Little & Rubin 1987; Schafer 1997).”
Background
Research works printed but not linked: 2
- Herzog, T. N., and D. B. Rubin. 1983. Using multiple imputations to handle nonresponse in sample surveys. Incomplete Data in Sample Surveys 2:209-245.
- van Buuren, S., and K. Groothuis-Oudshoorn. 2011. Multivariate Imputation by Chained Equations in R. Journal of Statistical Software 45 (3):1-67.
Cited by
Sorted by citations
A survey on missing data in machine learning
Emmanuel, T., Maupong, T., Mpoeleng, D., Semong, T., Mphago, B., & Tabona, O. (2021) · Journal of Big Data · doi:10.1186/s40537-021-00516-9 · Cited by 1,067
Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges
Bischl, B., Binder, M., Lang, M., Pielok, T., Richter, J., Coors, S., Thomas, J., Ullmann, T., Becker, M., Boulesteix, A., et al. (2023) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1484 · Cited by 1,262
SICE: an improved missing data imputation technique
Khan, S. I., & Hoque, A. S. M. L. (2020) · Journal Of Big Data · doi:10.1186/s40537-020-00313-w · Cited by 298
A Benchmark for Data Imputation Methods
Jäger, S., Allhorn, A., & Bießmann, F. (2021) · Frontiers in Big Data · doi:10.3389/fdata.2021.693674 · Cited by 268
“Others are benchmark studies and compare imputation strategies (Poulos and Valle, 2018; Jadhav et al., 2019; Woznica and Biecek, 2020).”
BackgroundMethodResultInfluentialMissing data in multi-omics integration: Recent advances through artificial intelligence
Flores, J. E., Claborne, D. M., Weller, Z. D., Webb-Robertson, B.-J. M., Waters, K. M., & Bramer, L. M. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1098308 · Cited by 181
Metadata
- Authors
- Jadhav, A., Pramod, D., & Ramanathan, K.
- Affiliations
- Symbiosis International University (IN)
- Corresponding author
- Jadhav, Anil
- Venue
- Applied Artificial Intelligence
- Publisher
- Informa UK Limited
- Published
- 2019-07-04
- Volume
- 33
- Issue
- 10
- Pages
- 913-933
- ISSN
- 0883-9514 (ISSN-L)
- 1087-6545
- OpenAlex
- W2955443275
- Cited by
- 471
- References
- 45
- Citations by year
- 2026: 64 · 2025: 124 · 2024: 102 · 2023: 97 · 2022: 44 · 2021: 27 · 2020: 13
- FWCI
- 16.42
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Applied Artificial Intelligence (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- missing data imputation, missing data, KNN imputation, mean imputation, median imputation, predictive mean matching, Bayesian linear regression, linear regression, imputation method comparison
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