Cited by 471Statistical Methods and Bayesian InferenceData Mining and Machine Learning ApplicationsMachine Learning and Data Classification

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Comparison of Performance of Data Imputation Methods for Numeric Dataset

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Jadhav, Anil, Pramod, Dhanya, Ramanathan, Krishnan·2019·Applied Artificial Intelligence

DOI 10.1080/08839514.2019.1637138

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Abstract

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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“The reasons for missing data can be equipment failure, data not entered due to misinterpretation, deleted as inconsistent with other data, certain data may not be considered significant while collecting the data[1].”
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“Assume the possibility of missing data value of K which is independent of the value of K or other variables in the data set[8].”
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Analysis of Incomplete Data Under Different Missingness Mechanism using Imputation Methods for Wheat Genotypes
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“Data mining merupakan proses penggalian informasi yang berguna dari kumpulan data serta mengekstraksinya agar menjadi informasi baru untuk pengambilan keputusan [2].”
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“Data imputation techniques play a vital role in such cases, involving estimating and filling in missing values, resulting in a more comprehensive and reliable dataset [4].”
Background
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“…rows or columns with missing values from the data set Bartlett et al. (2014) and imputation by filling in missing values using the mean or median Jadhav et al. (2019), k-Nearest Neighbor (KNN) imputation Jadhav et al. (2019) and Multiple Imputation by Chained Equation (MICE) (Z. Zhang, 2016).”
Method
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“Similar methods involve constant imputation [3], hot deck imputation, and cold deck imputation [4].”
Method
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“To ensure the continuous and complete PM 2.5 data of the target site and the auxiliary monitoring site, the KNN imputation method is utilized to fill the missing values [50, 51].”
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“However, techniques like interpolation (e.g., mean filling or insertion) have inherent limitations, as they struggle to fully capture the complex relationships between sequential data and missing values [5][6].”
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“This method was selected because the median provides a stable estimate of central tendency, which is less affected by outliers, ensuring that the imputed values reflect typical levels without being skewed by extreme values [5].”

Authors: Jadhav, A., Pramod, D., & Ramanathan, K.
Affiliations: Symbiosis International University (IN)
Corresponding author: Jadhav, Anil
ORCID: Jadhav, Anil
Pramod, Dhanya
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
DOI: 10.1080/08839514.2019.1637138
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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Comparison of Performance of Data Imputation Methods for Numeric Dataset

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