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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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Computational Biology and Chemistry, 2009
Single imputation methods have been wide-discussed topics among researchers in the field of bioinformatics. One major shortcoming of methods proposed until now is the lack of robustness considerations. Like all data, gene expression data can possess outlying values.
vanden Branden, Karlien +1 more
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Single imputation methods have been wide-discussed topics among researchers in the field of bioinformatics. One major shortcoming of methods proposed until now is the lack of robustness considerations. Like all data, gene expression data can possess outlying values.
vanden Branden, Karlien +1 more
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An Ensemble Method for Data Imputation
2019 IEEE International Conference on Healthcare Informatics (ICHI), 2019Healthcare analytics is transforming the healthcare industry, finding novel and useful patterns in patient data such as electronic health records (EHRs), to provide patients with improved care and service. Researchers train machine learning (ML) algorithms to discover new knowledge by mining patients’ clinical data to provide better care such as ...
Yichen Ding +3 more
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Privacy-Preserving Data Imputation
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06), 2006In this paper, we investigate privacy-preserving data imputation on distributed databases. We present a privacy-preserving protocol for filling in missing values using a lazy decision tree imputation algorithm for data that is horizontally partitioned between two parties.
Geetha Jagannathan, Rebecca N. Wright
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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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Joint Imputation of General Data
Journal of Survey Statistics and Methodology, 2023Abstract High-dimensional complex survey data of general structures (e.g., containing continuous, binary, categorical, and ordinal variables), such as the US Department of Defense’s Health-Related Behaviors Survey (HRBS), often confound procedures designed to impute any missing survey data.
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Deep Imputation of Temporal Data
2019 IEEE International Conference on Healthcare Informatics (ICHI), 2019Predictive modeling in healthcare has shown promise in various settings, such as early diagnosis, discovery of genotypephenotype associations, and the optimization of medical resource allocations [1]. Due to their data-driven nature, the effectiveness of these studies heavily relies on the quality of the collected data.
Chao Yan 0004 +4 more
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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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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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Classification Uncertainty of Multiple Imputed Data
2015 IEEE Symposium Series on Computational Intelligence, 2015Every classification model contains uncertainty. This uncertainty can be distributed evenly or into certain areas of feature space. In regular classification tasks, the uncertainty can be estimated from posterior probabilities. On the other hand, if the data set contains missing values, not all classifiers can be used directly.
Tuomo Alasalmi +3 more
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