Results 1 to 10 of about 287,343 (334)
Flexible Imputation of Missing Data [PDF]
Journal of Statistical Software, 2018 Hakan Demirtas
doaj +4 more sources
Missing data imputation using classification and regression trees [PDF]
PeerJ Computer ScienceBackground Missing data are common when analyzing real data. One popular solution is to impute missing data so that one complete dataset can be obtained for subsequent data analysis.
Cheng-Yang Chen, Yu-Wei Chang
doaj +3 more sources
SICE: an improved missing data imputation technique [PDF]
Journal of Big Data, 2020 In data analytics, missing data is a factor that degrades performance. Incorrect imputation of missing values could lead to a wrong prediction. In this era of big data, when a massive volume of data is generated in every second, and utilization of these ...
Shahidul Islam Khan +1 more
doaj +2 more sources
Discrete Missing Data Imputation Using Multilayer Perceptron and Momentum Gradient Descent [PDF]
Sensors, 2022 Data are a strategic resource for industrial production, and an efficient data-mining process will increase productivity. However, there exist many missing values in data collected in real life due to various problems. Because the missing data may reduce
Hu Pan +7 more
doaj +2 more sources
One Model to Synthesize Them All: Multi-Contrast Multi-Scale Transformer for Missing Data Imputation. [PDF]
IEEE Trans Med Imaging, 2023 Liu J +5 more
europepmc +3 more sources
Missing data imputation: focusing on single imputation. [PDF]
Ann Transl Med, 2016 Complete case analysis is widely used for handling missing data, and it is the default method in many statistical packages. However, this method may introduce bias and some useful information will be omitted from analysis. Therefore, many imputation methods are developed to make gap end. The present article focuses on single imputation.
Zhang Z.
europepmc +3 more sources
Self-Training With Quantile Errors for Multivariate Missing Data Imputation for Regression Problems in Electronic Medical Records: Algorithm Development Study. [PDF]
JMIR Public Health Surveill, 2021 Gwon H +8 more
europepmc +2 more sources
Advanced methods for missing values imputation based on similarity learning [PDF]
PeerJ Computer Science, 2021 The real-world data analysis and processing using data mining techniques often are facing observations that contain missing values. The main challenge of mining datasets is the existence of missing values.
Khaled M. Fouad +3 more
doaj +2 more sources
A comparison of imputation methods for categorical data
Informatics in Medicine Unlocked, 2023 Objectives: Missing data is commonplace in clinical databases, which are being increasingly used for research. Without giving any regard to missing data, results from analysis may become biased and unrepresentative.
Shaheen MZ. Memon +2 more
doaj +1 more source
IEEE Access, 2023 Missing data imputation is a technique to deal with incomplete datasets. Since many models and algorithms cannot be applied to data containing missing values, a pre-processing step needs to be performed to remove incomplete data or to estimate the ...
Reza Shahbazian, Sergio Greco
doaj +1 more source