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.
“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]
The Research Methodology section says the authors applied mean imputation to simulated missing values. The Missing Data Mechanisms section defines missing completely at random (MCAR), but the methodology does not say the simulated missingness was MCAR. (LLM, conf=0.82)
Experimental — accuracy not yet measuredKNN imputation has been shown to work well for numerical imputation on single datasets, 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.
“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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“Jadhav et al. [38] examine seven different imputation techniques using five small numerical datasets.”
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“Patterns of missing values were examined and median height (n = 411) and weight (n = 381) data related to the age groups was imputed [33].”
BackgroundMachine Learning Modelling for Predicting the Efficacy of Ionic Liquid-Aided Biomass Pretreatment
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“Three different data imputation techniques, i.e. k NN imputation, regression tree [21], and principal component analysis-based trimmed score regression (PCA-TSR) from missing data imputation (MDI) Toolbox [22], were considered.”
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“This approach was particularly useful for preserving the overall distribution and relationships within the data [30].”
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Comparison of Single and MICE Imputation Methods for Missing Values: A Simulation Study
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“ThemissingdatainthestudywerefirstfilledwiththeKNN 23,29 method.”
Missing value imputation Techniques: A Survey
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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.”
BackgroundMethodHard and Soft EM in Bayesian Network Learning from Incomplete Data
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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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Mindfulness vs Cognitive Behavioral Therapy for Chronic Low Back Pain Treated With Opioids
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Empirical Comparison of Imputation Methods for Multivariate Missing Data in Public Health
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“Many similar studies have compared methods available within the MICE R package, while some compared MICE to methods similar to FHDI, such as k-nearest neighbor (knn), and GERBIL, such as joint multivariate normal imputation (JM-MVN) [23–26].”
Leiby, B. D., & Ahner, D. K. (2023) · Journal Of Big Data · doi:10.1186/s40537-023-00698-4 · Cited by 18 · see citing works
“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].”
MethodMissing data imputation, prediction, and feature selection in diagnosis of vaginal prolapse
Fan, M., Peng, X., Niu, X., Cui, T., & He, Q. (2023) · BMC Medical Research Methodology · doi:10.1186/s12874-023-02079-0 · Cited by 18 · see citing works
“However, single imputation does not take into account the research objectives and individual differences, which may largely impact the estimation accuracy [18].”
BackgroundEvaluating missing data handling methods for developing building energy benchmarking models
Lee, K., Lim, H., Hwang, J., & Lee, D. (2024) · Energy · doi:10.1016/j.energy.2024.132979 · Cited by 18 · see citing works
Prognostic machine learning models for COVID‐19 to facilitate decision making
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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).”
BackgroundNumerical Data Imputation: Choose kNN over Deep Learning
Lalande, F., & Doya, K. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-17849-8_1 · Cited by 17 · see citing works
Nugroho, H., Utama, N. P., & Surendro, K. (2023) · Journal Of Big Data · doi:10.1186/s40537-022-00679-z · Cited by 18 · see citing works
“This indicated that the non-missing value mode of each variable was used to calculate the missing data [41–43].”
MethodA Survey of Machine Learning-Based Approaches for Missing Value Imputation
Gond, V. K., Dubey, A., & Rasool, A. (2021) · International Conference on Inventive Research in Computing Applications (ICIRCA) · doi:10.1109/icirca51532.2021.9544957 · Cited by 19 · see citing works
Multimodal Data Fusion to Track Students’ Distress during Educational Gameplay
Moon, J., Ke, F., Sokolikj, Z., & Dahlstrom‐Hakki, I. (2022) · Journal of Learning Analytics · doi:10.18608/jla.2022.7631 · Cited by 16 · see citing works
“Data imputation is generally used to reduce data bias and preserve all data instances in a computational way (Jadhav et al., 2019).”
MethodBinned Data Provide Better Imputation of Missing Time Series Data from Wearables
Chakrabarti, S., Biswas, N., Karnani, K., Padul, V. G., Jones, L., Kesari, S., & Ashili, S. (2023) · Sensors · doi:10.3390/s23031454 · Cited by 16 · see citing works
Advancing electron microscopy using deep learning
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“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…”
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“issue often found in medical data [40]–[42].”
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“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 · see citing works
Oh, J., & Min, D. (2024) · Energy · doi:10.1016/j.energy.2024.131621 · Cited by 16 · see citing works
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“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 · see citing works
“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
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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 · see citing works
“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
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Large-Scale Modeling of Sparse Protein Kinase Activity Data
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A Generalized GNN-Transformer-Based Radio Link Failure Prediction Framework in 5G RAN
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ChatGPT-based biological and psychological data imputation
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Applying Machine Learning Algorithms to Predict the Size of the Informal Economy
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Evaluating refactorings for disciplining #ifdef annotations: An eye tracking study with novices
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COMPARISON OF SIMPLE MISSING DATA IMPUTATION TECHNIQUES FOR NUMERICAL AND CATEGORICAL DATASETS
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Explainable Ensemble Learning Approaches for Predicting the Compression Index of Clays
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“The KNN algorithm can be employed as a method for imputing missing values in the original datasets [35,36].”
- 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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