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 …
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 …
Short-term photovoltaic power forecasting based on VMD and ISSA-GRU
P Jia, H Zhang, X Liu, X Gong - IEEE Access, 2021 - ieeexplore.ieee.org
Photovoltaic (PV) power generation is affected by many meteorological factors and
environmental factors, which has obvious intermittent, random, and volatile characteristics …
environmental factors, which has obvious intermittent, random, and volatile characteristics …
Genomic data imputation with variational auto-encoders
Background As missing values are frequently present in genomic data, practical methods to
handle missing data are necessary for downstream analyses that require complete data …
handle missing data are necessary for downstream analyses that require complete data …
Wind power prediction with missing data using Gaussian process regression and multiple imputation
T Liu, H Wei, K Zhang - Applied Soft Computing, 2018 - Elsevier
Wind power prediction is important for smooth power generation from wind turbines. Due to
the characteristics of volatility and indirectness of wind power, it is difficult to achieve high …
the characteristics of volatility and indirectness of wind power, it is difficult to achieve high …
An LDA–SVM machine learning model for breast cancer classification
Breast cancer is a prevalent disease that affects mostly women, and early diagnosis will
expedite the treatment of this ailment. Recently, machine learning (ML) techniques have …
expedite the treatment of this ailment. Recently, machine learning (ML) techniques have …
[HTML][HTML] A bi-objective k-nearest-neighbors-based imputation method for multilevel data
We propose a bi-objective algorithm based on the k-nearest neighbors (biokNN) method to
perform imputation of missing values for data with multilevel structures with continuous …
perform imputation of missing values for data with multilevel structures with continuous …
Machine learning-based imputation soft computing approach for large missing scale and non-reference data imputation
Missing data is a common problem in real-world data sets and it is amongst the most
complex topics in computer science and many other research domains. The common ways …
complex topics in computer science and many other research domains. The common ways …