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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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“38,39 The R (R Foundation for Statistical Computing, Vienna, Austria) packages naniar and visdat were used to assess randomness of missing data, and the VIM package of R was used to impute missing data using k-nearest neighbors.”
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“For the missing data in the above months, we used the mean interpolation method to supplement it (Jadhav et al., 2019).”
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“However, these methods can lead to bias, especially if the proportion of the missing values is significant [6].”
BackgroundGao, W., Xie, J., Ke, Y., Tian, M., Zeng, Z., Ma, X., & Zhi, M. (2022) · Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine · doi:10.1177/09544119231206456 · Cited by 3
“6,7 Of these three mechanisms, MNAR is so difficult to deal with that researches should try to avoid it at the design and data collection stage 8,9 ; MCAR rarely exists and is often difficult to assume and realize 10,11 ; and MAR was assumed to be the basis of most of the filling methodologies in…”
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“Hot Deck Imputation (Similarity) [3] No complex mathematical calculation is required It can be computationally incapable.”
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“In a numerical field, “0” can be used to fill in the blanks [9].”
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“These methods are simple, but they may lead to a change in the shape of the data distribution especially when the missing values are large in number [17].”
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“%) and non-normal distributions, ensuring a robust imputation approach for the dataset (21).”
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“33,34 .”
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“3) and other state-of-art imputation methods namely mean [35], Linear interpolation (LI) [46, 47], median [35], KNNI [36], k-means imputation (KMI) [23], FCM imputation (FKMI) [23], Iterative fuzzy clustering (IFC) [15], and Linear interpolation-based FCM (LIFCM) [31] as illustrated in subsection…”
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“The researchers employed an acceptable statistical method of imputation to manage the missing data (Jadhav et al., 2019), where the lowest (or least impactful) scores for the scales of job stress, well-being, and organizational characteristics were imputed for “preferred not to answer” responses.”
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“Additionally, Jadhav et al. conducted experiments with missing rates ranging from 10% to 50%, and their results demonstrated that k-NN outperforms all other ML models [33].”
MethodSong, Z., Zhou, Y., Cheng, W. D., Liang, F., & Zhang, C. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.047034 · Cited by 3
A Comparative Analysis of the Imputing of Missing Data on Air Pollution
Gayathri, M., & Kavitha, V. (2024) · International Conference on Networking and Communications (ICNWC) · doi:10.1109/icnwc60771.2024.10537524 · Cited by 2
“Mode imputation is not suitable for variables that are continuous, as they usually don't have a mode [22].”
BackgroundRevisiting the Problem of Missing Values in High-Dimensional Data and Feature Selection Effect
Elia, M. G., & Duan, W. (2024) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-63211-2_16 · Cited by 2
Data Imputation Using Correlation-Based Machine Learning Algorithms
Devi, B. A., & Karthik, N. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-64836-6_44 · Cited by 2
Anomaly Signal Imputation Using Latent Coordination Relations
Chalongvorachai, T., & Woraratpanya, K. (2024) · IEEE Access · doi:10.1109/access.2024.3448236 · Cited by 2
“…information loss [4], [5], [6]; (ii) Imputing missing values with fixed numbers or the mean/median, potentially oversimplifying dataset variability [7], [8], [9]; and (iii) Hot-deck and cold-deck imputation, utilizing attributes from other datasets to fill missing data, thereby introducing…”
MethodKarimi, F., Mehrpanah, A., & Rawassizadeh, R. (2024) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2024.104784 · Cited by 2
Visual Data Restoration for Pipeline Inspection Robots by Compressed Sensing
Zamora, J. D., & Tang, C. (2024) · International Conference on Robotics, Control and Automation (ICRCA) · doi:10.1109/icrca60878.2024.10649096 · Cited by 2
“Case deletion [9] is the most popular method in this category and consists in eliminate from the data set all the elements that have missing values and analyse the retained data.”
MethodEarly Prediction of Stroke using XGBoost classification
Husin, H. S., Azman, A., Hamzah, N., & Kumar, P. A.-L. A. (2024) · International Visualization, Informatics and Technology Conference (IVIT) · doi:10.1109/ivit62102.2024.10692877 · Cited by 2
“Median imputation is done by calculating mean and median value of the feature in the dataset (Jadhav et al., 2019).”
MethodMatsuo, K., Aihara, H., Suehiro, E., Shiomi, N., Yatsushige, H., Hirota, S., Hasegawa, S., Karibe, H., Miyata, A., Kawakita, K., et al. (2024) · Neurosurgery · doi:10.1227/neu.0000000000003238 · Cited by 2
Schwartz, A. V., Sant, K. E., & George, U. Z. (2025) · Toxicological Sciences · doi:10.1093/toxsci/kfaf069 · Cited by 3
“Missing values within the feature space were handled using median imputation (Jadhav et al. 2019) to ensure the completeness and robustness of the dataset.”
Eram, A. F., Mahmud, A. S., Khadem, M. M., & Ihsan, M. A. (2025) · Neuroscience Informatics · doi:10.1016/j.neuri.2025.100215 · Cited by 2
Sabat, N. K., Nayak, R., Pati, U. C., & Das, S. K. (2025) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2025.110603 · Cited by 2
Haynes, C. J., Beck, A. K., Kelly, P. J., Kaip, D., Wells, M., Issa, J., Kelly, D., Hides, L. M., & Larance, B. (2025) · Drug and Alcohol Review · doi:10.1111/dar.70011 · Cited by 2
“Two participants missing three or fewer PREMAT item responses out of the 23 total items (i.e., 10% of total PREMAT items) had values for these items imputed based on the mean of their completed PREMAT items [40, 41].”
Addressing Missing Data in a Healthcare Dataset Using an Improved kNN Algorithm
Thomas, T., & Rajabi, E. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-77977-1_17 · Cited by 2
“…is getting popular due to its applicability. k-Nearest Neighbours(kNN), being one of the simplest and non-parametric instance-based approaches, is widely used in missing value imputation problems[6]. kNN based imputation methods are easy to implement and perform well in a variety of scenarios [6].”
BackgroundMetode Imputasi pada Data Debit Daerah Aliran Sungai Opak, Provinsi DI Yogyakarta
Irnawan, F. D., Hidayah, I., & Nugroho, L. E. (2021) · Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI) · doi:10.22146/jnteti.v10i4.2430 · Cited by 3
Amphan, S., & Songmuang, P. (2023) · IEEE International Conference on Cybernetics and Innovations (ICCI) · doi:10.1109/icci57424.2023.10112242 · Cited by 2
Imputation Techniques in Machine Learning – A Survey
Christobel, E. A. A. (2023) · International Journal on Recent and Innovation Trends in Computing and Communication · doi:10.17762/ijritcc.v11i10.8662 · Cited by 2
Roch-Dupré, D., Aracil, E., & Calvo-Bascones, P. (2024) · Structural Change and Economic Dynamics · doi:10.1016/j.strueco.2024.08.007 · Cited by 2
The K Nearest Neighbor Algorithm for Imputation of Missing Longitudinal Prenatal Alcohol DataPreprint
Sania, A., Pini, N., Nelson, M. E., Myers, M. M., Shuffrey, L. C., Lucchini, M., Elliott, A. J., Odendaal, H. J., & Fifer, W. P. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4065215 · Cited by 2
The K Nearest Neighbor Algorithm for Imputation of Missing Longitudinal Prenatal Alcohol DataPreprint
Sania, A., Pini, N., Nelson, M. E., Myers, M. D., Shuffrey, L. C., Lucchini, M., Elliott, A. J., Odendaal, H. J., & Fifer, W. P. (2021) · Research Square · doi:10.21203/rs.3.rs-32456/v2 · Cited by 2
Hybrid Machine Learning for Matchmaking in Digital Business Ecosystems
Benramdane, M. K., Bouzefrane, S., Banerjee, S., Maupas, H., & Kornyshova, E. (2023) · IGI Global eBooks · doi:10.4018/978-1-7998-9220-5.ch168 · Cited by 2
Conradi, H. S. W., Nguyen, T., Lawal, O. A., & Carlson, L. E. (2025) · Global Advances in Integrative Medicine and Health · doi:10.1177/27536130251367051 · Cited by 1
“62,63 To account for normality violations, bootstrapped estimates of the structural models were obtained.”
End-to-End Automated Machine Learning System for Supervised Learning Problems
Bindila, M.-B., & Negru, M. (2021) · IEEE International Conference on Intelligent Computer Communication and Processing (ICCP) · doi:10.1109/iccp53602.2021.9733439 · Cited by 1
McGuire, A., Kriegshauser, K., & Blossom, J. B. (2023) · Journal of Emotional and Behavioral Disorders · doi:10.1177/10634266231154205 · Cited by 1
“Missing data were managed using k-nearest neighbor imputation by taking the weighted average of k = 10 nearest neighbors, as this method provides an appropriate imputation of both continuous and discrete variables for analyses involving interactions (e.g., Jadhav et al., 2019).”
Simulation analysis of an adjusted gravity model for hospital admissions robust to incomplete data
Latruwe, T., der Wee, M. V., Vanleenhove, P., Michielsen, K., Verbrugge, S., & Colle, D. (2023) · BMC Medical Research Methodology · doi:10.1186/s12874-023-02033-0 · Cited by 1
“Widely used imputation techniques are mean imputation, median imputation, k-nearest neighbours (kNN) imputation, predictive mean matching (pmm) imputation, Bayesian Linear regression, non-Bayesian Linear regression, and Sample imputation methods [6].”
BackgroundMethodForecasting a Journal Impact Factor Under Missing Values Based on Machine Learning
Hua, V., & Huynh, B. (2024) · IEEE Access · doi:10.1109/access.2024.3416345 · Cited by 1
“Traditional methods like Mean, Median, and Interpolation [11], [12], [13] offer limited solutions, effective only for small missing data.”
MethodAffinity-Enriched Plasma Proteomics for Biomarker Discovery in Abdominal Aortic Aneurysms
Palstrøm, N. B., Nielsen, K. B., Campbell, A. J., Soerensen, M., Rasmussen, L. M., Lindholt, J. S., & Beck, H. C. (2024) · Proteomes · doi:10.3390/proteomes12040037 · Cited by 1
“Imputation of missing values in the proteomics data was performed prior to feature selection using a commonly applied imputation method based on the k-nearest neighbors (kNN) algorithm [18,23,24] using k = 10.”
Predix, C. J., Lease, A. M., Mi-hyun, K., & Kwon, K. (2025) · The Journal of Early Adolescence · doi:10.1177/02724316241311934 · Cited by 1
“Each missing value was replaced with the mean of the non-missing values for the corresponding variable (Jadhav et al., 2019).”
ICU Admission Prediction for Patients With Kawasaki Disease or MIS-C Using Machine Learning
Woo, J., Mosier, R., Mukherjee, R., Harahsheh, A. S., Jain, S., Raghuveer, G., Sundaram, B., Lee, S., Portman, M. A., Dahdah, N., et al. (2025) · JACC Advances · doi:10.1016/j.jacadv.2025.101621 · Cited by 1
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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