Missing value imputation: a review and analysis of the literature (2006–2017)
WC Lin, CF Tsai - Artificial Intelligence Review, 2020 - Springer
Missing value imputation (MVI) has been studied for several decades being the basic
solution method for incomplete dataset problems, specifically those where some data …
solution method for incomplete dataset problems, specifically those where some data …
[HTML][HTML] Missing value imputation affects the performance of machine learning: A review and analysis of the literature (2010–2021)
Recently, numerous studies have been conducted on Missing Value Imputation (MVI),
intending the primary solution scheme for the datasets containing one or more missing …
intending the primary solution scheme for the datasets containing one or more missing …
Learning representations for time series clustering
Time series clustering is an essential unsupervised technique in cases when category
information is not available. It has been widely applied to genome data, anomaly detection …
information is not available. It has been widely applied to genome data, anomaly detection …
DeepCC: a novel deep learning-based framework for cancer molecular subtype classification
Molecular subtyping of cancer is a critical step towards more individualized therapy and
provides important biological insights into cancer heterogeneity. Although gene expression …
provides important biological insights into cancer heterogeneity. Although gene expression …
Exact p-values for pairwise comparison of Friedman rank sums, with application to comparing classifiers
Abstract Background The Friedman rank sum test is a widely-used nonparametric method in
computational biology. In addition to examining the overall null hypothesis of no significant …
computational biology. In addition to examining the overall null hypothesis of no significant …
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 …
[HTML][HTML] AI applications in functional genomics
We review the current applications of artificial intelligence (AI) in functional genomics. The
recent explosion of AI follows the remarkable achievements made possible by “deep …
recent explosion of AI follows the remarkable achievements made possible by “deep …
A class center based approach for missing value imputation
CF Tsai, ML Li, WC Lin - Knowledge-Based Systems, 2018 - Elsevier
Missing value imputation (MVI) is the major solution method for dealing with incomplete
dataset problems in which the missing attribute values are replaced from a chosen set of …
dataset problems in which the missing attribute values are replaced from a chosen set of …
Adversarial joint-learning recurrent neural network for incomplete time series classification
Incomplete time series classification (ITSC) is an important issue in time series analysis
since temporal data often has missing values in practical applications. However, integrating …
since temporal data often has missing values in practical applications. However, integrating …
Fault feature recovery with Wasserstein generative adversarial imputation network with gradient penalty for rotating machine health monitoring under signal loss …
Rotating machine health monitoring systems sometimes suffer from large segments of
continuous missing data in practical applications, which may lead to incorrect health …
continuous missing data in practical applications, which may lead to incorrect health …