Systematic review of using machine learning in imputing missing values
Missing data are a universal data quality problem in many domains, leading to misleading
analysis and inaccurate decisions. Much research has been done to investigate the different …
analysis and inaccurate decisions. Much research has been done to investigate the different …
[HTML][HTML] Missing signal imputation for multi-channel sensing signals on rotary machinery by tensor factorization
Multi-channel sensor fusion can be challenging for real-time machinery fault identification
and diagnosis when a substantial amount of missing data exists. Usually, some (or even all) …
and diagnosis when a substantial amount of missing data exists. Usually, some (or even all) …
[PDF][PDF] Clustering-based hybrid approach for multivariate missing data imputation
A Dubey, A Rasool - … Journal of Advanced Computer Science and …, 2020 - academia.edu
In the era of big data, a significant amount of data is produced in many applications areas.
However due to various reasons including sensor failures, communication failures …
However due to various reasons including sensor failures, communication failures …
Investigation of Low-Frequency Data Significance in Electric Vehicle Drivetrain Durability Development
M Li, FKD Noering, Y Öngün, M Appelt… - World Electric Vehicle …, 2024 - mdpi.com
The digitalization of the automotive industry presents significant potential for technical
advantages, such as the online collection of customer driving data. These data can be used …
advantages, such as the online collection of customer driving data. These data can be used …
Deep and structure-preserving autoencoders for clustering data with missing information
Most real-life data suffer from missing values. Here we deal with the problem of exploratory
analysis, via clustering, of data with missing values. For this we need an effective …
analysis, via clustering, of data with missing values. For this we need an effective …
REMIAN: Real-time and error-tolerant missing value imputation
Missing value (MV) imputation is a critical preprocessing means for data mining.
Nevertheless, existing MV imputation methods are mostly designed for batch processing …
Nevertheless, existing MV imputation methods are mostly designed for batch processing …
Missing value recovery for encoder signals using improved low-rank approximation
M Zhao, Y Li, S Chen, B Li - Mechanical Systems and Signal Processing, 2020 - Elsevier
Rotary encoders have been increasingly equipped in high precision machinery, and their
data missing may pose great challenges for both numeric control and health monitoring. In …
data missing may pose great challenges for both numeric control and health monitoring. In …
Multiview data fusion technique for missing value imputation in multisensory air pollution dataset
The missing readings in various sensors of air pollution monitoring stations is a common
issue. Those missing sensor readings may greatly influence the performance of monitoring …
issue. Those missing sensor readings may greatly influence the performance of monitoring …
An Experimental Evaluation of Imputation Models for Spatial-Temporal Traffic Data
Traffic data imputation is a critical preprocessing step in intelligent transportation systems,
enabling advanced transportation services. Despite significant advancements in this field …
enabling advanced transportation services. Despite significant advancements in this field …
[PDF][PDF] Missing value imputation a review
D Das, M Nayak, SK Pani - Int J Comput Sci Eng, 2019 - researchgate.net
Accepted: 15/Apr/2019, Published: 30/Apr/2019 Abstract-The problems of missing values in
the field of data mining have become emerging areas of research in recent years. It has …
the field of data mining have become emerging areas of research in recent years. It has …