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Cross-Modal Graph Attention for Bridge SHM Data Imputation. [PDF]
Sensors (Basel)Xiong J, Hu L, Meng X, An X, Xie Y.
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Adapting tree-based multiple imputation methods for multilevel data? A simulation study. [PDF]
Behav Res MethodsFöge N +4 more
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SGA-DT: An adaptive fusion framework for missing data imputation and interpretable healthcare classification. [PDF]
PLoS OneJena M, Dehuri S, Cho SB.
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Review: A gentle introduction to imputation of missing values
Journal of Clinical Epidemiology, 2006In most situations, simple techniques for handling missing data (such as complete case analysis, overall mean imputation, and the missing-indicator method) produce biased results, whereas imputation techniques yield valid results without complicating the analysis once the imputations are carried out. Imputation techniques are based on the idea that any
Karel G Moons, , A Rogier T Donders
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Missing value imputation strategies for metabolomics data
Electrophoresis, 2015The origin of missing values can be caused by different reasons and depending on these origins missing values should be considered differently and dealt with in different ways. In this research, four methods of imputation have been compared with respect to revealing their effects on the normality and variance of data, on statistical significance and on
Emily Armitage +2 more
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Missing Value Imputation for Diabetes Prediction
2022 International Joint Conference on Neural Networks (IJCNN), 2022This research is supported, in part, by the National Research Foundation (NRF), Singapore under its AI Singapore Programme (AISG Award No: AISG-GC-2019-003). H. Qian thanks the support from the Wallenberg-NTU Presidential Postdoctoral Fellowship.
Fei Luo +8 more
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Sequential imputation for missing values
Computational Biology and Chemistry, 2007As missing values are often encountered in gene expression data, many imputation methods have been developed to substitute these unknown values with estimated values. Despite the presence of many imputation methods, these available techniques have some disadvantages. Some imputation techniques constrain the imputation of missing values to a limited set
Sabine Verboven +2 more
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On-line imputation for missing values
2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2017Missing values are widespread in many real world applications. It is often preferred to receive in real-time the high quality complete tuples, rather than an incomplete one containing null attribute values. The requirements for high quality and real-time response make the task of missing value imputation much challenging.
Fengfeng Fan +2 more
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Neighborhood-aware autoencoder for missing value imputation
2020 28th European Signal Processing Conference (EUSIPCO), 2021Missing values are a fundamental issue in many applications by constraining the application of different learning methods or by impairing the attained results. Many solutions have been proposed by relying on statistical or machine learning techniques. However, in most cases, the results are not yet satisfactory.
Helena Aidos, Pedro Tomás
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Intelligent imputation technique for missing values
2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2016Missing value is a widespread problem for data quality because most of the statistical procedures require a value for each variable. The missing value may lead to biased parameter estimates, and may result in degradation of data quality. Imputation has been used to replace the missing data by plausible estimation.
Tahani Aljuaid, Sreela Sasi
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