Results 281 to 290 of about 24,713,244 (334)

Self-Supervision Improves Diffusion Models for Tabular Data Imputation

International Conference on Information and Knowledge Management
The ubiquity of missing data has sparked considerable attention and focus on tabular data imputation methods. Diffusion models, recognized as the cutting-edge technique for data generation, demonstrate significant potential in tabular data imputation ...
Yixin Liu, Thalaiyasingam Ajanthan, Hisham Husain, Vu Nguyen   +3 more
semanticscholar   +1 more source

Joint Imputation of General Data

Journal of Survey Statistics and Methodology, 2023
Abstract 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.
openaire   +2 more sources

Private Data Imputation

arXiv.org
Data imputation is an important data preparation task where the data analyst replaces missing or erroneous values to increase the expected accuracy of downstream analyses.
Abdelkarim Kati, Florian Kerschbaum, M. Blanton   +2 more
semanticscholar   +1 more source

Missing-Data Imputation With Position-Encoding Denoising Autoencoders for Industrial Processes

IEEE Transactions on Instrumentation and Measurement
Missing values are a common occurrence in industrial datasets, resulting from the multiple sampling rates, sensor malfunctions, and transmission errors, whose presence can significantly affect the accuracy of data-driven models.
Chen Ou, Hongqiu Zhu, Yuri A. W. Shardt, Lingjian Ye, Xiaofeng Yuan, Yalin Wang, Chunhua Yang, Weihua Gui   +7 more
semanticscholar   +1 more source

Deep Imputation of Temporal Data

2019 IEEE International Conference on Healthcare Informatics (ICHI), 2019
Predictive 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, Cheng Gao, Xinmeng Zhang, You Chen 0001, Bradley A. Malin   +4 more
openaire   +1 more source

Missing Data Imputation with Uncertainty-Driven Network

Proc. ACM Manag. Data
We study the problem of missing data imputation, which is a fundamental task in the area of data quality that aims to impute the missing data to achieve the completeness of datasets.
Jianwei Wang, Ying Zhang, Kai Wang, Xuemin Lin, Wenjie Zhang   +4 more
semanticscholar   +1 more source

Missing data imputation and sensor self-validation towards a sustainable operation of wastewater treatment plants via deep variational residual autoencoders.

Chemosphere, 2021
Missing data imputation and automatic fault detection of wastewater treatment plant (WWTP) sensors are crucial for energy conservation and environmental protection.
Abdulrahman H. Ba-Alawi, Jorge Loy-Benitez, Sangyun Kim, C. Yoo   +3 more
semanticscholar   +1 more source

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
openaire   +1 more source

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.
openaire   +2 more sources

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.
openaire   +2 more sources