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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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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