Visual analysis for panel data imputation with Bayesian network

Bayesian network is derived from conditional probability and is useful in inferring the next state of the currently observed variables. If data are missed or corrupted during data collection or transfer, the characteristics of the original data may be distorted and biased. Therefore, predicted value...

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Bibliographic Details
Published in:The Journal of supercomputing 2022-02, Vol.78 (2), p.1759-1782
Main Authors: Yeon, Hanbyul, Seo, Seongbum, Son, Hyesook, Jang, Yun
Format: Article
Language:English
Subjects:
Advances in Big Data and Deep Learning
Bayesian analysis
Compilers
Computer Science
Conditional probability
Data analysis
Data collection
Datasets
Decision analysis
Decision making
Interpreters
Longitudinal studies
Machine learning
Missing data
Processor Architectures
Programming Languages
Statistical analysis
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Summary:Bayesian network is derived from conditional probability and is useful in inferring the next state of the currently observed variables. If data are missed or corrupted during data collection or transfer, the characteristics of the original data may be distorted and biased. Therefore, predicted values from the Bayesian network designed with missing data are not reliable. Various techniques have been studied to resolve the imperfection in data using statistical techniques or machine learning, but since the complete data are unknown, there is no optimal way to impute missing values. In this paper, we present a visual analysis system that supports decision-making to impute missing values occurring in panel data. The visual analysis system allows data analysts to explore the cause of missing data in panel datasets. The system also enables us to compare the performance of suitable imputation models with the Bayesian network accuracy and the Kolmogorov–Smirnov test. We evaluate how the visual analysis system supports the decision-making process for the data imputation with datasets in different domains.
ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-021-03934-x