Results 211 to 220 of about 161,037 (263)

Missing Data Imputation

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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Missing Data Imputation Techniques

International Journal of Business Intelligence and Data Mining, 2007
Intelligent 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, 2013
Missing 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, 2002
AbstractMissing 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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Imputation and Missing Data

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, Matthew Pincus, Jenny Libien   +2 more
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Imputation of Missing Data

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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Imputing missing yield trial data

Theoretical and Applied Genetics, 1990
The Additive Main effects and Multiplicative Interaction (AMMI) statistical model has been demonstrated effective for understanding genotype-environment interactions in yields, estimating yields more accurately, selecting superior genotypes more reliably, and allowing more flexible and efficient experimental designs. However, AMMI had required data for
H G, Gauch, R W, Zobel
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Privacy-preserving imputation of missing data

Data & Knowledge Engineering, 2008
Handling missing data is a critical step to ensuring good results in data mining. Like most data mining algorithms, existing privacy-preserving data mining algorithms assume data is complete. In order to maintain privacy in the data mining process while cleaning data, privacy-preserving methods of data cleaning will be required.
Geetha Jagannathan, Rebecca N. Wright
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Cooperative Clustering Missing Data Imputation

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2020
Missing 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, Roozbeh Razavi-Far, Mehrdad Saif   +2 more
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