Results 31 to 40 of about 16,362,739 (323)
Imputing missing values using cumulative linear regression
CAAI Transactions on Intelligence Technology, 2019 The concept of missing data is important to apply statistical methods on the dataset. Statisticians and researchers may end up to an inaccurate illation about the data if the missing data are not handled properly.
Samih M. Mostafa
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Missing the missing values: The ugly duckling of fairness in machine learning
International Journal of Intelligent Systems, 2021 Nowadays, there is an increasing concern in machine learning about the causes underlying unfair decision making, that is, algorithmic decisions discriminating some groups over others, especially with groups that are defined over protected attributes ...
Fernando Martínez-Plumed +3 more
semanticscholar +1 more source
Large‐scale data visualization with missing values
Technological and Economic Development of Economy, 2006 Visualization of large‐scale data inherently requires dimensionality reduction to 1D, 2D, or 3D space. Autoassociative neural networks with a bottleneck layer are commonly used as a nonlinear dimensionality reduction technique.
Sergiy Popov
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Metabolites, 2020 To ensure scientific reproducibility of metabolomics data, alternative statistical methods are needed. A paradigm shift away from the p-value toward an embracement of uncertainty and interval estimation of a metabolite’s true effect size may lead to ...
Christopher E. Gillies +7 more
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Energy-based temporal neural networks for imputing missing values [PDF]
, 2012 Imputing missing values in high dimensional time series is a difficult problem. There have been some approaches to the problem [11,8] where neural architectures were trained as probabilistic models of the data. However, we argue that this approach is not
G.E. Hinton +9 more
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Missing Value Imputation With Unsupervised Backpropagation [PDF]
, 2013 Many data mining and data analysis techniques operate on dense matrices or complete tables of data. Real-world data sets, however, often contain unknown values.
Gashler, Michael S. +3 more
core +1 more source
Analyzing the impact of missing values and selection bias on fairness
International Journal of Data Science and Analysis, 2021 Algorithmic decision making is becoming more prevalent, increasingly impacting people’s daily lives. Recently, discussions have been emerging about the fairness of decisions made by machines.
Yanchen Wang, L. Singh
semanticscholar +1 more source
Missing values imputation using Fuzzy K-Top Matching Value
Journal of King Saud University: Computer and Information Sciences, 2023 Missing data occurs when variables or observations are missing. Researchers exclude or impute influenced variables and data. This study proposes Fuzzy K-Top Matching Value (FKTM) for missing value imputation.
Azza Ali +3 more
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Dealing with Missing Data using a Selection Algorithm on Rough Sets
International Journal of Computational Intelligence Systems, 2018 This paper discusses the so-called missing data problem, i.e. the problem of imputing missing values in information systems. A new algorithm, called the ARSI algorithm, is proposed to address the imputation problem of missing values on categorical ...
Jonathan Prieto-Cubides, Camilo Argoty
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Adaptive imputation of missing values for incomplete pattern classification [PDF]
, 2016 In classification of incomplete pattern, the missing values can either play a crucial role in the class determination, or have only little influence (or eventually none) on the classification results according to the context.
Dezert, Jean +3 more
core +4 more sources