Results 1 to 10 of about 175,631 (262)
Multiple imputation with missing data indicators [PDF]
Statistical Methods in Medical Research, 2021 Multiple imputation is a well-established general technique for analyzing data with missing values. A convenient way to implement multiple imputation is sequential regression multiple imputation, also called chained equations multiple imputation. In this approach, we impute missing values using regression models for each variable, conditional on the ...
Lauren J Beesley, Allison W. Kurian
exaly +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
missForestPredict-Missing data imputation for prediction settings. [PDF]
PLoS OnePrediction models are used to predict an outcome based on input variables. Missing data in input variables often occur at model development and at prediction time. The missForestPredict R package proposes an adaptation of the missForest imputation algorithm that is fast, user-friendly and tailored for prediction settings.
Albu E, Gao S, Wynants L, Van Calster B.
europepmc +5 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
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Missing Data Imputation for Supervised Learning [PDF]
Applied Artificial Intelligence, 2018 Missing data imputation can help improve the performance of prediction models in situations where missing data hide useful information. This paper compares methods for imputing missing categorical data for supervised classification tasks.
Jason Poulos, Rafael Valle
doaj +4 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
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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
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
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DEGAIN: Generative-Adversarial-Network-Based Missing Data Imputation
Information, 2022 Insights and analysis are only as good as the available data. Data cleaning is one of the most important steps to create quality data decision making. Machine learning (ML) helps deal with data quickly, and to create error-free or limited-error datasets.
Reza Shahbazian, Irina Trubitsyna
doaj +1 more source