Graphical models for processing missing data
This article reviews recent advances in missing data research using graphical models to
represent multivariate dependencies. We first examine the limitations of traditional …
represent multivariate dependencies. We first examine the limitations of traditional …
A systematic review of machine learning-based missing value imputation techniques
T Thomas, E Rajabi - Data Technologies and Applications, 2021 - emerald.com
Purpose The primary aim of this study is to review the studies from different dimensions
including type of methods, experimentation setup and evaluation metrics used in the novel …
including type of methods, experimentation setup and evaluation metrics used in the novel …
Graphical models for inference with missing data
We address the problem of deciding whether there exists a consistent estimator of a given
relation Q, when data are missing not at random. We employ a formal representation …
relation Q, when data are missing not at random. We employ a formal representation …
Handling missing data through deep convolutional neural network
The presence of missing data is a challenging issue in processing real-world datasets. It is
necessary to improve the data quality by imputing the missing values so that effective …
necessary to improve the data quality by imputing the missing values so that effective …
Missing value imputation using a novel grey based fuzzy c-means, mutual information based feature selection, and regression model
AM Sefidian, N Daneshpour - Expert Systems with Applications, 2019 - Elsevier
The presence of missing values in real-world data is not only a prevalent problem but also
an inevitable one. Therefore, missing values should be handled carefully before the mining …
an inevitable one. Therefore, missing values should be handled carefully before the mining …
缺失数据处理方法研究综述.
熊中敏， 郭怀宇， 吴月欣 - Journal of Computer Engineering …, 2021 - search.ebscohost.com
大数据时代, 数据爆炸式的增长, 数据获取变得更容易的同时数据缺失现象也更加普遍.
数据的缺失极大地降低了数据的实用性. 数据缺失问题的处理成为大数据处理的热点研究课题 …
数据的缺失极大地降低了数据的实用性. 数据缺失问题的处理成为大数据处理的热点研究课题 …
Machine learning improves prediction of delayed cerebral ischemia in patients with subarachnoid hemorrhage
Background and purpose Delayed cerebral ischemia (DCI) is a severe complication in
patients with aneurysmal subarachnoid hemorrhage. Several associated predictors have …
patients with aneurysmal subarachnoid hemorrhage. Several associated predictors have …
Multiple imputation ensembles (MIE) for dealing with missing data
A Aleryani, W Wang, B De La Iglesia - SN Computer Science, 2020 - Springer
Missing data is a significant issue in many real-world datasets, yet there are no robust
methods for dealing with it appropriately. In this paper, we propose a robust approach to …
methods for dealing with it appropriately. In this paper, we propose a robust approach to …
Missing data imputation for classification problems
A Choudhury, MR Kosorok - arXiv preprint arXiv:2002.10709, 2020 - arxiv.org
Imputation of missing data is a common application in various classification problems where
the feature training matrix has missingness. A widely used solution to this imputation …
the feature training matrix has missingness. A widely used solution to this imputation …
Estimating missing data using novel correlation maximization based methods
AM Sefidian, N Daneshpour - Applied Soft Computing, 2020 - Elsevier
The accurate estimation of missing data plays a vital role in ensuring a high level of data
quality. The missing values should be imputed before performing data mining, machine …
quality. The missing values should be imputed before performing data mining, machine …