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International Journal of Decision Support System Technology, 2022
Many real world datasets may contain missing values for various reasons. These incomplete datasets can pose severe issues to the underlying machine learning algorithms and decision support systems. It may result in high computational cost, skewed output and invalid deductions. Various solutions exist to mitigate this issue; the most popular strategy is
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Many real world datasets may contain missing values for various reasons. These incomplete datasets can pose severe issues to the underlying machine learning algorithms and decision support systems. It may result in high computational cost, skewed output and invalid deductions. Various solutions exist to mitigate this issue; the most popular strategy is
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Missing Data Imputation Techniques
International Journal of Business Intelligence and Data Mining, 2007Intelligent data analysis techniques are useful for better exploring real-world data sets. However, the real-world data sets always are accompanied by missing data that is one major factor affecting data quality. At the same time, good intelligent data exploration requires quality data.
Qinbao Song, Martin J. Shepperd
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Missing Data and Multiple Imputation
JAMA Pediatrics, 2013Missing data can result in biased estimates of the association between an exposure X and an outcome Y. Even in the absence of bias, missing data can hurt precision, resulting in wider confidence intervals. Analysts should examine the missing data pattern and try to determine the causes of the missingness.
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Cooperative Clustering Missing Data Imputation
2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2020Missing data imputation is a critical part of data cleaning tasks and vital for learning from incomplete data. This paper proposes a novel cooperative clustering imputation (CCI) method to estimate missing values. The proposed method aims to find a better clustering model and donor for imputation, comparing with individual clustering algorithms.
Daoming Wan +2 more
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Multiple imputation for missing data†‡
Research in Nursing & Health, 2002AbstractMissing data occur frequently in survey and longitudinal research. Incomplete data are problematic, particularly in the presence of substantial absent information or systematic nonresponse patterns. Listwise deletion and mean imputation are the most common techniques to reconcile missing data.
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2017
The presence of missing data is a big challenge for statisticians, especially if the distribution of the missing values is not completely random. Analysis performed on datasets with missing data can lead to erroneous conclusions and significant bias in the results.
Amir Momeni +2 more
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The presence of missing data is a big challenge for statisticians, especially if the distribution of the missing values is not completely random. Analysis performed on datasets with missing data can lead to erroneous conclusions and significant bias in the results.
Amir Momeni +2 more
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Imputation of Missing Ages in Pedigree Data
Human Heredity, 2007<i>Background:</i> In human pedigree data age at disease occurrence frequently is missing and is imputed using various methods. However, little is known about the performance of these methods when applied to families. In particular, there is little information about the level of agreement between imputed and actual values of temporal data ...
Raymond R, Balise +6 more
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2011
Below is a subset of data from a smoking cessation study for smokers newly diagnosed with cancer.Patients were assessed for anxiety and depression at baseline using the Hospital Anxiety and Depression Scale (Zigmond and Snaith 1983), at least 7 days before they were hospitalized for surgery.
Yuelin Li, Jonathan Baron
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Below is a subset of data from a smoking cessation study for smokers newly diagnosed with cancer.Patients were assessed for anxiety and depression at baseline using the Hospital Anxiety and Depression Scale (Zigmond and Snaith 1983), at least 7 days before they were hospitalized for surgery.
Yuelin Li, Jonathan Baron
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Imputation of Missing Data in Industrial Databases
Applied Intelligence, 1999A limiting factor for the application of IDA methods in many domains is the incompleteness of data repositories. Many records have fields that are not filled in, especially, when data entry is manual. In addition, a significant fraction of the entries can be erroneous and there may be no alternative but to discard these records.
Kamakshi Lakshminarayan +2 more
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