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Capturing Missing Tuples and Missing Values [PDF]
ACM Transactions on Database Systems, 2016 Databases in real life are often neither entirely closed-world nor entirely open-world. Databases in an enterprise are typically partially closed , in which a part of the data is constrained by master data that contains complete information about the enterprise in certain aspects. It has been
Deng, Ting, Fan, Wenfei, Geerts, Floris
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Systematic Review of Using Machine Learning in Imputing Missing Values
IEEE Access, 2022 Missing data are a universal data quality problem in many domains, leading to misleading analysis and inaccurate decisions. Much research has been done to investigate the different mechanisms of missing data and the proper techniques in handling various ...
Mustafa Alabadla +12 more
semanticscholar +1 more source
Fairness without Imputation: A Decision Tree Approach for Fair Prediction with Missing Values [PDF]
AAAI Conference on Artificial Intelligence, 2021 We investigate the fairness concerns of training a machine learning model using data with missing values. Even though there are a number of fairness intervention methods in the literature, most of them require a complete training set as input.
Haewon Jeong, Hao Wang, F. Calmon
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Missing Values in Panel Data Unit Root Tests
Econometrics, 2022 Missing data or missing values are a common phenomenon in applied panel data research and of great interest for panel data unit root testing. The standard approach in the literature is to balance the panel by removing units and/or trimming a common time ...
Yiannis Karavias +2 more
doaj +1 more source
PhyloPars: estimation of missing parameter values using phylogeny. [PDF]
, 2009 A wealth of information on metabolic parameters of a species can be inferred from observations on species that are phylogenetically related. Phylogeny-based information can complement direct empirical evidence, and is particularly valuable if experiments
Brandt, B.W., Bruggeman, J., Heringa, J.
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Journal of Clinical Epidemiology, 2022 OBJECTIVE To compare the validity and robustness of five methods for handling missing characteristics when using cardiovascular disease risk prediction models for individual patients in a real-world clinical setting.
Gijs F. N. Berkelmans +9 more
semanticscholar +1 more source
Mathematics, 2023 Navigating the intricate world of data analytics, one method has emerged as a key tool in confronting missing data: multiple imputation. Its strength is further fortified by its powerful variant, robust imputation, which enhances the precision and ...
Matthias Templ
doaj +1 more source
Clustering mixed numerical and categorical data with missing values
Information Sciences, 2021 This paper proposes a novel framework for clustering mixed numerical and categorical data with missing values. It integrates the imputation and clustering steps into a single process, which results in an algorithm named C lustering M ixed Numerical and ...
Duy-Tai Dinh, V. Huynh, S. Sriboonchitta
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Imputation with the R Package VIM
Journal of Statistical Software, 2016 The package VIM (Templ, Alfons, Kowarik, and Prantner 2016) is developed to explore and analyze the structure of missing values in data using visualization methods, to impute these missing values with the built-in imputation methods and to verify the ...
Alexander Kowarik, Matthias Templ
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Urban traffic flow prediction: a dynamic temporal graph network considering missing values
International Journal of Geographical Information Science, 2022 Accurate traffic flow prediction on the urban road network is an indispensable function of Intelligent Transportation Systems (ITS), which is of great significance for urban traffic planning.
Peixiao Wang +3 more
semanticscholar +1 more source