A review of the current publication trends on missing data imputation over three decades: direction and future research

Adnan, Farah Adibah; Jamaludin, Khairur Rijal; Muhamad, Wan Zuki Azman Wan; Miskon, Suraya

doi:10.1007/s00521-022-07702-7

Cited by 12 publications

(4 citation statements)

References 78 publications

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“…Adnan et al (2022) provided a comprehensive review of studies on missing data imputation methods for classification problems. VOSviewer and Harzing Publish or Perish software were used for the analysis [30].…”

Section: Related Workmentioning

confidence: 99%

“…Additionally, the findings provide tools to create effective models for filling in missing data [28] and suggest a set of guidelines for using machine learning to address this challenge [29]. These studies are a valuable resource for quickly understanding the available imputation methods [30], especially within the machine learning (ML) domain. Investigations into data imputation are also performed from multiple angles, examining the types of methods used, experimental setups, and innovative evaluation metrics [31].…”

Section: Introductionmentioning

confidence: 99%

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A Comprehensive Bibliometric Analysis of Missing Value Imputation

Nugroho,

Surendro

2024

IEEE Access

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Data quality plays a crucial role in tasks, such as enhancing the accuracy of data analytics and avoiding the accumulation of redundant data. One of the significant challenges in data quality is dealing with missing data, which has been extensively explored by the scholarly community and has resulted in a significant increase in related publications. It is important to recognize that the landscape of missing data in computer science offers numerous opportunities for further research. However, upon closer examination of existing studies, it becomes evident that many have not fully utilized bibliometric analysis tools and software for comprehensive literature reviews. Therefore, this study aims to explore the essential characteristics, trends, and prevailing themes in the field of missing data imputation. Through a thorough bibliometric analysis, this study demonstrated the evolution of knowledge and key focal points in the field of missing data imputation. The analysis consisted of 352 journal papers in social sciences published between 2012 and 2023, all centered on missing data imputation. Among these publications, "IEEE Access" has become a highly respected source. To systematically explore various aspects of missing data imputation, a conceptual framework was used to uncover potential research directions and underlying themes. Ultimately, a thematic map serves as a valuable tool for providing a comprehensive understanding, categorizing significant concepts into basic or overarching, developing, or declining, central, highly developed, and isolated themes. These overarching and underlying themes offer valuable insights and pave the way for prospective directions and critical areas of study.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Comprehensive Bibliometric Analysis of Missing Value Imputation

Nugroho,

Surendro

2024

IEEE Access

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“…Consequently, a complex approach to the imputation of those missing values is developed to accurately accommodate incomplete data. To impute missing data with plausible values, numerous machine learning algorithms have been proposed [ 9 , 10 ].…”

Section: - Introductionmentioning

confidence: 99%

Comparison performance of the Bayesian Approach with the Weibull and Birnbaum-Saunders distributions in imputation of time-to-event censors

Shahmirzalou,

Rasekhi,

Jafari Khaledi

et al. 2024

PLoS ONE

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Almost all survival data is censored, and censor imputation is necessary. This study aimed to investigate the performance of the Bayesian Approach (BA) in the imputation of censored records in simulated and Breast Cancer (BC) data. Due to the difference in the distribution of time to event in survival analysis, two well-known the Weibull and Birnbaum-Saunders (BS) distributions have been used to test the performance of the BA. For each of the censored, 10,000 times were simulated using the BA in R and BUGS software, and their median or mean was imputed instead of each censor. The eligibility of both imputation methods was investigated using different curves, different censoring percentages, and sample sizes, as well as the Deviance Information Criteria (DIC), Effective Sample Size, and the Geweke diagnostic in simulated and especially real BC data. The BC data, which contains 220 patients who were identified and followed up between 2015 and 2023, was made accessible on February 1, 2023. The Kaplan-Meier, the BA, and other survival curves were drawn for the observed times. Findings indicated that the performance of the BA under the Weibull and BS distributions in simulated data is similar. The DIC index in the BC data under the BS distribution (1510) is less than the Weibull distribution (1698). Therefore, the BS distribution is preferred over the Weibull for imputation of censoring times in real BC data.

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“…The usual practice is to delete missing values directly, [7] but it will cause the model analysis to be biased or misleading. [18,19] The other practice is to fill in missing values with zeros, but this would result in an unrealistic model that interferes with decision-making. Therefore, an appropriate method of missing value interpolation should be used, [20] interpolating data that conforms to the structure of the production data and the mechanism of smelting.…”

Section: Introductionmentioning

confidence: 99%

A CatBoost‐Based Modeling Approach for Predicting End‐Point Carbon Content of Electric Arc Furnace

Lu,

Zhu,

Jiang

et al. 2024

steel research int.

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Developing the prediction model of the end‐point carbon content of the electric arc furnace (EAF) is an effective way to reduce the adjustment frequency of liquid steel composition and shorten the smelting time. Previous data‐driven models lack effective handling of the missing values in EAF production data. This may be the main reason why model accuracy is difficult to improve. This article proposes a novel modeling method based on the CatBoost algorithm with two‐stage optimization. In the preprocessing session, empirical and empirical‐cumulative‐distribution‐based outlier detection (ECOD) methods are utilized to extract input features and reject outliers. The end‐point carbon content prediction model is built based on CatBoost. The generative adversarial imputation nets (GAIN) method is used in the first optimization stage to handle the missing values. In the second optimization stage, recursive feature elimination (RFE) is used to select the final features, and whale optimization algorithm (WOA) is used to optimize the parameters of the CatBoost model. After verification with actual production data, the two‐stage optimized CatBoost model demonstrates excellent performance compared with other methods, with an R2 of 0.903, mean absolute error of 0.021, root mean squared error of 0.043, and 90.34% hit ratio within ±0.05% error range.

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A review of the current publication trends on missing data imputation over three decades: direction and future research

Cited by 12 publications

References 78 publications

A Comprehensive Bibliometric Analysis of Missing Value Imputation

A Comprehensive Bibliometric Analysis of Missing Value Imputation

Comparison performance of the Bayesian Approach with the Weibull and Birnbaum-Saunders distributions in imputation of time-to-event censors

A CatBoost‐Based Modeling Approach for Predicting End‐Point Carbon Content of Electric Arc Furnace

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