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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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“Imputation methods considered included zero, half-minimum, median, and mean substitution, Bayesian principal component analysis (BPCA) and MISSFOREST, as these techniques have proven useful in other studies [29, 30].”
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“These methods, such as k-nearest neighbor imputation (KNN) (Hamzah et al., 2020; Han et al., 2022; Jadhav et al., 2019), random forest imputation (RF) (Brei-man, 2001; Pinthong et al., 2024), and a series of Bayes-based optimization algorithms and models (Chen & Sun, 2022; Lai & Kuok, 2019)…”
An optimal imputation algorithm for reducing bias and errors in missing data handling for AI models
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An analytics-driven hybrid method for multi-item demand forecasting in supply chains
Lingkon, M. L. R., Hossain, M. S., & Chakrabortty, R. K. (2026) · Supply Chain Analytics · doi:10.1016/j.sca.2026.100194 · Cited by 5
IMICE: An Improved Missing Data Imputation Using Machine Learning
Varma, K. M., Nayak, S., & Khilar, P. M. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-99-9442-7_15 · Cited by 5
Wenigmann, M., Weiß, J., & Heidelberg, R. (2024) · Journal of Criminal Justice · doi:10.1016/j.jcrimjus.2024.102214 · Cited by 6
Vázquez, B., Montiel, N. H., Pérez-González, J., & Haro, P. (2025) · PLoS ONE · doi:10.1371/journal.pone.0315843 · Cited by 5
“In simple imputation, each missing value is replaced by a descriptive statistic (mean, median, or most frequent) alongside each feature [27].”
Wabina, R. S., Looareesuwan, P., Sonsilphong, S., Teza, H., Ponthongmak, W., McKay, G. J., Attia, J., Pattanateepapon, A., Panitchote, A., & Thakkinstian, A. (2025) · Journal Of Big Data · doi:10.1186/s40537-025-01136-3 · Cited by 5
“The performance of different imputation methods can be influenced by various factors such as the amount of missing data [25], its underlying distribution [26], and the complexity of the relationships between observed predictors and missing variables [27].”
Dog visits in nursing homes – increase complexity or keep it simple? A randomised controlled study
Thodberg, K., Videbech, P., Hansen, T. G. B., Pedersen, A. B., & Christensen, J. W. (2021) · PLoS ONE · doi:10.1371/journal.pone.0251571 · Cited by 8
“For those with less than 50% missed items, the values of the missed items were substituted with the median of this item across participants by imputation [31].”
MethodDetection of Alzheimer’s Disease Progression Using Integrated Deep Learning Approaches
Shetty, J., Shetty, N. P., Kothikar, H., Mowla, S., Anand, A., & Hegde, V. (2023) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2023.039206 · Cited by 5
Hsu, J.-H., Wu, C., Wang, W.-K., Su, H.-Y., Lin, E. C., & Chen, P. S. (2023) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2023.3299607 · Cited by 6
Forecasting Students’ Success To Graduate Using Predictive Analytics
Almonteros, J. R., Matias, J. B., & Pitao, J. V. S. (2024) · International Journal of Computing and Digital Systems · doi:10.12785/ijcds/150151 · Cited by 5
“In addition, KNN imputation also performed best among other methods in a numeric dataset [29].”
BackgroundMethodKapalaga, G., Kivunike, F. N., Kerfua, S. D., Jjingo, D., Biryomumaisho, S., Rutaisire, J., Ssajjakambwe, P., Mugerwa, S., & Kiwala, Y. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1446368 · Cited by 5
“Despite more sophisticated methods available, mean imputation was chosen for its simplicity and effectiveness in maintaining data consistency and integrity (Rubin, 2018; Jadhav et al., 2019).”
MethodImpacts of Missing Data Imputation on Resilience Evaluation for Water Distribution System
Ghimire, A. B., Magar, B. A., Parajuli, U., & Shin, S. (2024) · Urban Science · doi:10.3390/urbansci8040177 · Cited by 5
Supervision and Wellness: A Survey of Human Service Practitioners
Sewell, K. M., van Rensburg, M. J., Peddle, M., Alschech, J., & Asakura, K. (2024) · Human Services Organizations Management Leadership & Governance · doi:10.1080/23303131.2024.2434527 · Cited by 12
“Mean imputation was used to replace missing values (Jadhav et al., 2019).”
Müting, L., & Mußhoff, O. (2024) · Agricultural Systems · doi:10.1016/j.agsy.2024.104224 · Cited by 5
Magyari‐Sáska, Z., Haidu, I., & Magyari-Sáska, A. (2025) · Applied Sciences · doi:10.3390/app15031242 · Cited by 5
Kim, Y., Chae, M., Cho, N., Gil, H., & Lee�, H. (2022) · Mathematics · doi:10.3390/math10244633 · Cited by 5
Tsai, Y.-T., Fulcher, I., Li, T., Sukums, F. K., & Hedt‐Gauthier, B. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e16244 · Cited by 5
Aracri, F., Bianco, M. G., Quattrone, A., & Sarica, A. (2023) · International Workshop on Biomedical Applications, Technologies and Sensors (BATS) · doi:10.1109/bats59463.2023.10303151 · Cited by 5
“Two studies [8], [17], emphasized the significance of using reliable imputation approaches to increase classification accuracy and robustness.”
BackgroundMethodGrigoroff, L., Masuda, R., Lindon, J. C., Kadyrov, J., Nicholson, J. K., Holmes, E. C., & Wist, J. (2025) · PLoS ONE · doi:10.1371/journal.pone.0335852 · Cited by 4
Natural carbonation of concrete: a data-driven analysis
Chan, T., Yio, M. H. N., & Wong, H. S. (2025) · Cement and Concrete Composites · doi:10.1016/j.cemconcomp.2025.106436 · Cited by 4
Duflot, T., Fayette, L., Konecki, C., Seurat, J., Féliu, C., Scala‐Bertola, J., & Djerada, Z. (2025) · The AAPS Journal · doi:10.1208/s12248-025-01066-1 · Cited by 4
Identifying Suitability for Data Reduction in Imbalanced Time-Series Datasets
Sanderson, D., & Kalganova, T. (2025) · AI · doi:10.3390/ai6050098 · Cited by 4
Varol, B., Ömürlü, İ. K., & Türe, M. (2025) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610918.2025.2524548 · Cited by 4
Vershinina, O., Sabbatinelli, J., Bonfigli, A. R., Colombaretti, D., Giuliani, A., Krivonosov, M., Trukhanov, A., Franceschi, C., Ivanchenko, M., & Olivieri, F. (2025) · Frontiers in Endocrinology · doi:10.3389/fendo.2025.1689312 · Cited by 4
“Finally, remaining missing values were imputed using the k-nearest neighbors algorithm (k=5), which has been well-established in various studies (28, 29).”
Bhushan, M., Vyas, S., Mall, S., & Negi, A. (2023) · International Journal of Systems Assurance Engineering and Management · doi:10.1007/s13198-023-02160-3 · Cited by 4
“Afterwards, to fill in the missing values mean imputation technique was utilized (Jadhav et al. 2019).”
MethodGolds, L., Gillespie‐Smith, K., & MacBeth, A. M. (2024) · Infant Mental Health Journal · doi:10.1002/imhj.22112 · Cited by 5
Revolutionizing Missing Data Handling with RFKFCM: Random Forest-based Kernelized Fuzzy C-Means
Jyoti, Singh, J., & Gosain, A. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.03.196 · Cited by 4
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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