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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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“Other studies include Jadhav et al. [36], who compared imputation methods on five numeric datasets and based on the root mean square error (RMSE) statistic found that k nearest neighbour (kNN) imputation outperformed another six methods comprising mean, median, predictive mean matching (PMM),…”
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“KNN and RF have been reported to have excellent imputation performance in relevant studies 5–10, 27 , but these researches were based on real-data applications with continuous Mode imputation merely considered the correlation between variables, though the imputation accuracy was stable in most…”
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“In particular, we assessed and compared the performance of I mean and MF through their imputation error, calculated as the mean absolute error (MAE), the root mean square error (RMSE) ) [16], [17], where, the larger the value, the greater the error.”
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“However, this can markedly reduce the amount of data and it is widely documented to introduce bias in both learning and inference, see, for instance [2,20].”
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“Although no single imputation method succeeds at being the best in all imputation applications [2], some studies demonstrate k-nearest neighbors as the best single imputation method and predictive mean matching (pmm) as the best multiple imputation method for the datasets considered [3].”
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“However, single imputation does not take into account the research objectives and individual differences, which may largely impact the estimation accuracy [18].”
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“KNN is a supervised learning algorithm that, given the sample, checks the distribution of surrounding data and classifies it under the category with the highest frequency (Jadhav, Pramod, and Ramanathan 2019).”
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“This indicated that the non-missing value mode of each variable was used to calculate the missing data [41–43].”
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“Data imputation is generally used to reduce data bias and preserve all data instances in a computational way (Jadhav et al., 2019).”
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Relational Data Cleaning Meets Artificial Intelligence: A Survey
Zhu, J., Zhao, X., Sun, Y., Song, S., & Yuan, X. (2024) · Data Science and Engineering · doi:10.1007/s41019-024-00266-7 · Cited by 26
“Additionally, there are other methods utilizing LSA (Least Squares Adjustment) [121], PMM (Predictive mean matching) [122], RRPI (Random Recursive Partitioning Imputer) [123] which employs random recursive partitioning, CLRMC (Correlation-based Low-rank Matrix Completion) [124] which employs…”
Alkhodari, M., Jelinek, H. F., Saleem, S., Hadjileontiadis, L. J., & Khandoker, A. H. (2021) · IEEE Access · doi:10.1109/access.2021.3114029 · Cited by 16
“issue often found in medical data [40]–[42].”
Li, Z., Dong, A., & Zhou, J. (2020) · Computational and Mathematical Methods in Medicine · doi:10.1155/2020/5294840 · Cited by 16
Yu, J., Peng, X., Zhou, R., Zhu, T., & Hao, X. (2024) · International Journal of Surgery · doi:10.1097/js9.0000000000002203 · Cited by 15
Gupta, T., Bhatia, R., & Sharma, S. (2025) · Scientific Reports · doi:10.1038/s41598-025-06610-9 · Cited by 16
da Costa, J. A. S., Gheyi, R., Castor, F., de Oliveira, P. R. F., Ribeiro, M., & Fonseca, B. (2023) · Empirical Software Engineering · doi:10.1007/s10664-023-10311-0 · Cited by 15
“The PMM method uses the predictive mean matching (Jadhav et al 2019) to impute univariate missing data.”
MethodGuo, S., & Wang, Y. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2023.108079 · Cited by 15
Oh, J., & Min, D. (2024) · Energy · doi:10.1016/j.energy.2024.131621 · Cited by 16
Mobasheri, F., Hosseinpoor, M., Yahia, A., & Pourkamali‐Anaraki, F. (2025) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-025-10284-x · Cited by 16
“In contrast, multiple imputation does not replace a single value for each missing observation; rather, it substitutes multiple plausible values to account for uncertainty regarding the correct values to impute [ 219 ].”
Mueller, B., Street, W. N., Carnahan, R. M., & Lee, S. (2023) · Acta Psychiatrica Scandinavica · doi:10.1111/acps.13551 · Cited by 15
“38 Possible outliers were identified with the IQR Extreme Value analysis and treated as missing values.”
MethodA proposed approach for diabetes diagnosis using neuro-fuzzy technique
Alasaady, M. T., Aris, T. N. M., Sharef, N. M., & Hamdan, H. (2022) · Bulletin of Electrical Engineering and Informatics · doi:10.11591/eei.v11i6.4269 · Cited by 14
Hu, X., & Assaad, R. H. (2023) · Journal of Bridge Engineering · doi:10.1061/jbenf2.beeng-6012 · Cited by 14
Dehkordi, M. K., Sattari, F., & Lefsrud, L. (2025) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2025.110971 · Cited by 14
Missing Data Analysis in Regression
Marcelino, C. G., Leite, G. M. C., Celes, P., & Pedreira, C. E. (2022) · Applied Artificial Intelligence · doi:10.1080/08839514.2022.2032925 · Cited by 14
“According to Jadhav, Pramod, and Ramanathan (2019) missing data is a common problem faced by researchers and data scientists.”
BackgroundComparison of Seventeen Missing Value Imputation Techniques
Hameed, W. M., & Ali, N. A. (2022) · Journal of Hunan University Natural Sciences · doi:10.55463/issn.1674-2974.49.7.4 · Cited by 14
Large-Scale Modeling of Sparse Protein Kinase Activity Data
Luukkonen, S., Meijer, E., Tricarico, G. A., Hofmans, J., Stouten, P. F. W., van Westen, G. J. P., & Lenselink, E. B. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00132 · Cited by 17
Maggio, L. G. D., Gastaldi, C., Renzo, D. A., Delprete, C., & Furgiuele, F. M. (2025) · Engineering With Computers · doi:10.1007/s00366-025-02139-7 · Cited by 13
A Generalized GNN-Transformer-Based Radio Link Failure Prediction Framework in 5G RAN
Hasan, K. A., Papry, K., Trappenberg, T. P., & Haque, I. (2025) · IEEE Transactions on Machine Learning in Communications and Networking · doi:10.1109/tmlcn.2025.3575368 · Cited by 13
“The majority of statistical and machine learning algorithms lack robustness in handling missing values, thereby being susceptible to the impact of incomplete data [44].”
ChatGPT-based biological and psychological data imputation
Nazir, A., Cheema, M. N., & Wang, Z. (2023) · Meta-Radiology · doi:10.1016/j.metrad.2023.100034 · Cited by 13
Applying Machine Learning Algorithms to Predict the Size of the Informal Economy
Felix, J., Alexandre, M., & Lima, G. T. (2024) · Computational Economics · doi:10.1007/s10614-024-10593-6 · Cited by 14
Chowdhury, A. H., & Rahman, M. S. (2025) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0012800 · Cited by 13
“The rationale for employing mean imputation stems from its ease of use and ability to substitute missing values based on data distribution [43].”
DeVille, D. C., Khalsa, S. S., Lapidus, R. C., White, E. J., Paulus, M. P., & Aupperle, R. L. (2021) · Behavior Therapy · doi:10.1016/j.beth.2021.01.006 · Cited by 14
Evaluating refactorings for disciplining #ifdef annotations: An eye tracking study with novices
da Costa, J. A. S., Gheyi, R., Ribeiro, M., Apel, S., Alves, V., Fonseca, B., Medeiros, F., & Garcia, A. (2021) · Empirical Software Engineering · doi:10.1007/s10664-021-10002-8 · Cited by 13
COMPARISON OF SIMPLE MISSING DATA IMPUTATION TECHNIQUES FOR NUMERICAL AND CATEGORICAL DATASETS
Gautam, R., & Latifi, S. (2023) · Journal of Research in Engineering and Applied Sciences · doi:10.46565/jreas.202381468-475 · Cited by 12
Explainable Ensemble Learning Approaches for Predicting the Compression Index of Clays
Ge, Q., Xia, Y., Shu, J., Li, J., & Sun, H. (2024) · Journal of Marine Science and Engineering · doi:10.3390/jmse12101701 · Cited by 12
“The KNN algorithm can be employed as a method for imputing missing values in the original datasets [35,36].”
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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