Results 11 to 20 of about 179,516 (314)
Methods to Handle Incomplete Data
MAMC Journal of Medical Sciences, 2020 Context: The major question for data analysis is determining the appropriate analytic approach in the presence of incomplete observations. The most common solution to handle missing data in a data set is imputation, where missing values are estimated and
Vinny Johny +2 more
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IGANI: Iterative Generative Adversarial Networks for Imputation With Application to Traffic Data
IEEE Access, 2021 Increasing use of sensor data in intelligent transportation systems calls for accurate imputation algorithms that can enable reliable traffic management in the occasional absence of data.
Amir Kazemi, Hadi Meidani
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Sensors, 2020 Building operation data are important for monitoring, analysis, modeling, and control of building energy systems. However, missing data is one of the major data quality issues, making data imputation techniques become increasingly important.
Liang Zhang
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An efficient ensemble method for missing value imputation in microarray gene expression data
BMC Bioinformatics, 2021 Background The genomics data analysis has been widely used to study disease genes and drug targets. However, the existence of missing values in genomics datasets poses a significant problem, which severely hinders the use of genomics data.
Xinshan Zhu +5 more
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TIME SERIES IMPUTATION USING VAR-IM (CASE STUDY: WEATHER DATA IN METEOROLOGICAL STATION OF CITEKO)
Barekeng, 2022 Univariate imputation methods are defined as imputation methods that only use the information of the target variable to estimate missing values.
Muhammad Edy Rizal +2 more
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Fractional Imputation Algorithm for Incomplete Data Based on Multi-Model Fusion [PDF]
Jisuanji gongcheng, 2023 Missing data imputation is an important step in data mining from incomplete datasets. Existing imputation algorithms cannot effectively utilize samples with high missing rates, which results in the equivalent processing of samples with different missing ...
Liangshan SHAO, Songze ZHAO
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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
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RIIM:Real-Time Imputation Based on Individual Models [PDF]
Jisuanji kexue, 2022 With the enrichment of data sources,data can be obtained easily but with low quality,resulting that the MVs are ubi-quitous and hard to avoid.Consequently,MV imputation has become one of the classical problems in the field of data quality mana-gement ...
LI Xia, MA Qian, BAI Mei, WANG Xi-te, LI Guan-yu, NING Bo
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Nonparametric Imputation by Data Depth [PDF]
Journal of the American Statistical Association, 2019 We present single imputation method for missing values which borrows the idea of data depth---a measure of centrality defined for an arbitrary point of a space with respect to a probability distribution or data cloud. This consists in iterative maximization of the depth of each observation with missing values, and can be employed with any properly ...
Mozharovskyi, Pavlo +2 more
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Missing Categorical Data Imputation and Individual Observation Level Imputation
Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 2014 Traditional missing data techniques of imputation schemes focus on prediction of the missing value based on other observed values. In the case of continuous missing data the imputation of missing values often focuses on regression models.
Pavel Zimmermann +2 more
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