Deep learning versus conventional methods for missing data imputation: A review and comparative study
Deep learning models have been recently proposed in the applications of missing data
imputation. In this paper, we review the popular statistical, machine learning, and deep …
imputation. In this paper, we review the popular statistical, machine learning, and deep …
[HTML][HTML] Maximizing information from chemical engineering data sets: Applications to machine learning
It is well-documented how artificial intelligence can have (and already is having) a big
impact on chemical engineering. But classical machine learning approaches may be weak …
impact on chemical engineering. But classical machine learning approaches may be weak …
[HTML][HTML] A benchmark for data imputation methods
S Jäger, A Allhorn, F Bießmann - Frontiers in big Data, 2021 - frontiersin.org
With the increasing importance and complexity of data pipelines, data quality became one of
the key challenges in modern software applications. The importance of data quality has …
the key challenges in modern software applications. The importance of data quality has …
A deep probabilistic transfer learning framework for soft sensor modeling with missing data
Soft sensors have been extensively developed and applied in the process industry. One of
the main challenges of the data-driven soft sensors is the lack of labeled data and the need …
the main challenges of the data-driven soft sensors is the lack of labeled data and the need …
Missing the missing values: The ugly duckling of fairness in machine learning
MP Fernando, F Cèsar, N David… - International Journal of …, 2021 - Wiley Online Library
Nowadays, there is an increasing concern in machine learning about the causes underlying
unfair decision making, that is, algorithmic decisions discriminating some groups over …
unfair decision making, that is, algorithmic decisions discriminating some groups over …
Missing value estimation using clustering and deep learning within multiple imputation framework
Missing values in tabular data restrict the use and performance of machine learning,
requiring the imputation of missing values. Arguably the most popular imputation algorithm …
requiring the imputation of missing values. Arguably the most popular imputation algorithm …
[HTML][HTML] Deep imputation of missing values in time series health data: A review with benchmarking
M Kazijevs, MD Samad - Journal of biomedical informatics, 2023 - Elsevier
The imputation of missing values in multivariate time series (MTS) data is a critical step in
ensuring data quality and producing reliable data-driven predictive models. Apart from many …
ensuring data quality and producing reliable data-driven predictive models. Apart from many …
A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sources
Electronic Health Records (EHRs) are a valuable asset to facilitate clinical research and
point of care applications; however, many challenges such as data privacy concerns impede …
point of care applications; however, many challenges such as data privacy concerns impede …
Toward Addressing Training Data Scarcity Challenge in Emerging Radio Access Networks: A Survey and Framework
The future of cellular networks is contingent on artificial intelligence (AI) based automation,
particularly for radio access network (RAN) operation, optimization, and troubleshooting. To …
particularly for radio access network (RAN) operation, optimization, and troubleshooting. To …
A survey of generative adversarial networks for synthesizing structured electronic health records
Electronic Health Records (EHRs) are a valuable asset to facilitate clinical research and
point of care applications; however, many challenges such as data privacy concerns impede …
point of care applications; however, many challenges such as data privacy concerns impede …