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View all- Li MLiu YZheng QLi GQin W(2023)An evolutionary computation classification method for high‐dimensional mixed missing variables dataElectronics Letters10.1049/ell2.1305859:24Online publication date: 12-Dec-2023
Four Factors Affecting Missing Data Imputation
Article No.: 26, Pages 1 - 2
Abstract
Missing data is a common problem in datasets and impacts the reliability of data analysis. Numerous methods to impute (i.e., predict and replace) missing values have been proposed. The quality of these imputed values depends on factors like correlation, percentage of missingness, or the mechanism behind the missing value. Despite comparative studies on imputation methods, conditions for their effectiveness and safe application lack dedicated investigation.
This research aims to systematically investigate the impact of four factors on imputation quality. We specifically investigate the extent to which (1) missing data mechanism, (2) variable distribution, (3) correlation, and (4) percentage of missingness affect the imputation quality of eight different machine-learning-based imputation methods. The evaluation will be done on both a synthetic dataset and a real-world dataset from voestalpine Stahl GmbH.
References
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Michal Bechny, Florian Sobieczky, Jürgen Zeindl, and Lisa Ehrlinger. 2021. Missing Data Patterns: From Theory to an Application in the Steel Industry. In 33rd International Conference on Scientific and Statistical Database Management. ACM, New York, NY, USA, 214–219. https://doi.org/10.1145/3468791.3468841
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Syed Imtiaz and Sirish Shah. 2008. Treatment of Missing Values in Process Data Analysis. The Canadian Journal of Chemical Engineering 86, 5 (2008), 838–858.
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Anil Jadhav, Dhanya Pramod, and Krishnan Ramanathan. 2019. Comparison of Performance of Data Imputation Methods for Numeric Dataset. Applied Artificial Intelligence 33, 10 (2019), 913–933.
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Sebastian Jäger, Arndt Allhorn, and Felix Bießmann. 2021. A Benchmark for Data Imputation Methods. Frontiers in Big Data 4 (2021).
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Roderick J.A. Little and Donald B. Rubin. 2020. Statistical Analysis with Missing Data. John Wiley & Sons, Hoboken, NJ, USA.
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Jason Poulos and Rafael Valle. 2018. Missing Data Imputation for Supervised Learning. Applied Artificial Intelligence 32, 2 (2018), 186–196.
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Stef van Buuren. 2022. MICE: Multiple imputation by Chained Equations. R Foundation for Statistical Computing. https://cran.r-project.org/web/packages/mice/mice.pdfR package version 3.15.0.
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Index Terms
- Four Factors Affecting Missing Data Imputation
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Published In
July 2023
232 pages
ISBN:9798400707469
DOI:10.1145/3603719
Copyright © 2023 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 27 August 2023
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- Refereed limited
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- Austrian Research Promotion Agency (FFG)
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SSDBM 2023
SSDBM 2023: 35th International Conference on Scientific and Statistical Database Management
July 10 - 12, 2023
CA, Los Angeles, USA
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Overall Acceptance Rate 56 of 146 submissions, 38%
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Cited By
View all- Li MLiu YZheng QLi GQin W(2023)An evolutionary computation classification method for high‐dimensional mixed missing variables dataElectronics Letters10.1049/ell2.1305859:24Online publication date: 12-Dec-2023
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