Results 41 to 50 of about 204,036 (213)
Effects of Different Missing Data Imputation Techniques on the Performance of Undiagnosed Diabetes Risk Prediction Models in a Mixed-Ancestry Population of South Africa. [PDF]
PLoS ONE, 2015 Imputation techniques used to handle missing data are based on the principle of replacement. It is widely advocated that multiple imputation is superior to other imputation methods, however studies have suggested that simple methods for filling missing ...
Katya L Masconi +3 more
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Imputation Using Training Labels and Classification via Label Imputation
IEEE AccessMissing data is a common problem in practice, and various imputation methods have been developed to deal with missing data. However, even though in many cases, the labels are available in the training data, the common practice of imputation usually only ...
Thu Nguyen +4 more
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MIAEC: Missing Data Imputation Based on the Evidence Chain
IEEE Access, 2018 Missing or incorrect data caused by improper operations can seriously compromise security investigation. Missing data can not only damage the integrity of the information but also lead to the deviation of the data mining and analysis.
Xiaolong Xu +4 more
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Practical strategies for handling breakdown of multiple imputation procedures
Emerging Themes in Epidemiology, 2021 Multiple imputation is a recommended method for handling incomplete data problems. One of the barriers to its successful use is the breakdown of the multiple imputation procedure, often due to numerical problems with the algorithms used within the ...
Cattram D. Nguyen +2 more
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IEEE Access, 2023 Missing data imputation is a technique to deal with incomplete datasets. Since many models and algorithms cannot be applied to data containing missing values, a pre-processing step needs to be performed to remove incomplete data or to estimate the ...
Reza Shahbazian, Sergio Greco
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Column-wise Guided Data Imputation [PDF]
Procedia Computer Science, 2017 Abstract This paper investigates data imputation techniques for pre-processing of dataset with missing values. The current literature is mainly focused on the overall accuracy, evaluated estimating the missing values on the dataset at hand, however the predictions can be suboptimal when considering the model performance for each feature.
Alessio Petrozziello, Ivan Jordanov
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, 2008 Missing data is a widespread problem that can affect the ability to use data to construct effective prediction systems. We investigate a common machine learning technique that can tolerate missing values, namely C4.5, to predict cost using six real world
Liu, J, Song, Q, Shepperd, MJ, Chen, X
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Random Forest variable importance with missing data [PDF]
, 2012 Random Forests are commonly applied for data prediction and interpretation. The latter purpose is supported by variable importance measures that rate the relevance of predictors. Yet existing measures can not be computed when data contains missing values.
Hapfelmeier, Alexander +2 more
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SoftwareXIn today's technological landscape, where data processing forms the backbone of modeling and predictive analytics, data imputation is crucial in filling missing values within datasets using statistical techniques.
Vartul Shrivastava, Shekhar Shukla
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Optimal Recovery of Missing Values for Non-Negative Matrix Factorization
IEEE Open Journal of Signal Processing, 2021 Missing values imputation is often evaluated on some similarity measure between actual and imputed data. However, it may be more meaningful to evaluate downstream algorithm performance after imputation than the imputation itself.
Rebecca Chen Dean, Lav R. Varshney
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