Results 21 to 30 of about 4,225,561 (321)
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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Principled Missing Data Treatments
Prevention Science, 2016 We review a number of issues regarding missing data treatments for intervention and prevention researchers. Many of the common missing data practices in prevention research are still, unfortunately, ill-advised (e.g., use of listwise and pairwise deletion, insufficient use of auxiliary variables).
Lang, Kyle, Little, Todd D.
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A new weighted NMF algorithm for missing data interpolation and its application to speech enhancement [PDF]
, 2019 In this paper we present a novel weighted NMF (WNMF) algorithm for interpolating missing data. The proposed approach has a computational cost equivalent to that of standard NMF and, additionally, has the flexibility to control the degree of interpolation
Gangashetty, S. +2 more
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Missing Data Analysis in Regression
Applied Artificial Intelligence, 2022 Many of the datasets in real-world applications contain incompleteness. In this paper, we approach the effects and possible solutions to incomplete databases in regression, aiming to bridge a gap between theoretically effective algorithms.
C. G. Marcelino +3 more
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Cognitive Diagnosis Modeling Incorporating Item-Level Missing Data Mechanism
Frontiers in Psychology, 2020 The aim of cognitive diagnosis is to classify respondents' mastery status of latent attributes from their responses on multiple items. Since respondents may answer some but not all items, item-level missing data often occur.
Na Shan +3 more
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Identifying research priorities for effective retention strategies in clinical trials
Trials, 2017 Background The failure to retain patients or collect primary-outcome data is a common challenge for trials and reduces the statistical power and potentially introduces bias into the analysis. Identifying strategies to minimise missing data was the second
Anna Kearney +7 more
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Data-Driven Method for Missing Harmonic Data Completion
IEEE Access, 2021 In large-scale applications, missing harmonic data during transmission is inevitable. This paper presents a novel approach for the completion of missing harmonic data based on a data-driven approach.
Rui Xu +4 more
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Automatic Classification of Variable Stars in Catalogs with missing data
, 2013 We present an automatic classification method for astronomical catalogs with missing data. We use Bayesian networks, a probabilistic graphical model, that allows us to perform inference to pre- dict missing values given observed data and dependency ...
Pichara, Karim, Protopapas, Pavlos
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Variable selection with Random Forests for missing data [PDF]
, 2013 Variable selection has been suggested for Random Forests to improve their efficiency of data prediction and interpretation. However, its basic element, i.e.
Hapfelmeier, Alexander, Ulm, Kurt
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Vietnam Journal of Computer Science, 2023 In intelligent information systems data play a critical role. The issue of missing data is one of the commonplace problems occurring in data collected in the real world. The problem stems directly from the very nature of data collection.
Mateusz Szczepański +3 more
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