Results 11 to 20 of about 204,036 (213)
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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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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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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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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In-Database Data Imputation [PDF]
Proceedings of the ACM on Management of DataMissing data is a widespread problem in many domains, creating challenges in data analysis and decision making. Traditional techniques for dealing with missing data, such as excluding incomplete records or imputing simple estimates (e.g., mean), are computationally efficient but may introduce bias and disrupt variable relationships, leading to ...
Massimo Perini, Milos Nikolic 0001
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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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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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Modern multiple imputation with functional data [PDF]
Stat, 2021 This work considers the problem of fitting functional models with sparsely and irregularly sampled functional data. It overcomes the limitations of the state‐of‐the‐art methods, which face major challenges in the fitting of more complex non‐linear models.
Aniruddha Rajendra Rao, Matthew Reimherr
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