Results 31 to 40 of about 4,243,996 (253)

Multiple Imputation Ensembles (MIE) for dealing with missing data [PDF]

, 2020
Missing data is a significant issue in many real-world datasets, yet there are no robust methods for dealing with it appropriately. In this paper, we propose a robust approach to dealing with missing data in classification problems: Multiple Imputation ...
A Farhangfar, AM Sefidian, B Schölkopf, C Cortes, CT Tran, DA Newman, DB Rubin, DB Rubin, DH Wolpert, EL Silva-Ramírez, GE Batista, GJ van der Heijden, H Gao, IH Witten, J Demšar, J Honaker, J Honaker, J Scheffer, JA Sterne, JL Schafer, JL Schafer, JR Quinlan, K Abayomi, KM Ting, L Breiman, L Breiman, L Rokach, M Fichman, M Khalilia, M Spratt, MA Klebanoff, MJ Azur, NJ Horton, PJ García-Laencina, PJ Kelly, PN Tan, RJ Little, S García, S Van Buuren, S Van Buuren, SS Chae, SS Choi, U Garciarena, V Vapnik, X Chen, Y Dong, Y Freund, Y He, Z Che, Z Liu   +49 more
core   +1 more source

Missing data imputation using classification and regression trees [PDF]

PeerJ Computer Science
Background Missing data are common when analyzing real data. One popular solution is to impute missing data so that one complete dataset can be obtained for subsequent data analysis.
Cheng-Yang Chen, Yu-Wei Chang
doaj   +2 more sources

Multiple imputation with missing indicators as proxies for unmeasured variables: simulation study

BMC Medical Research Methodology, 2020
Background Within routinely collected health data, missing data for an individual might provide useful information in itself. This occurs, for example, in the case of electronic health records, where the presence or absence of data is informative.
Matthew Sperrin, Glen P. Martin
doaj   +1 more source

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
doaj   +1 more source

High-Dimensional Matched Subspace Detection When Data are Missing [PDF]

, 2010
We consider the problem of deciding whether a highly incomplete signal lies within a given subspace. This problem, Matched Subspace Detection, is a classical, well-studied problem when the signal is completely observed. High- dimensional testing problems
Balzano, Laura, Nowak, Robert, Recht, Bejamin   +2 more
core   +3 more sources

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, G. M. C. Leite, P. Celes, C. E. Pedreira   +3 more
doaj   +1 more source

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, Na Shan, Xiaofei Wang, Xiaofei Wang   +3 more
doaj   +1 more source

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
core   +1 more source

Recursive kernel density estimators under missing data

, 2016
In this paper we propose an automatic bandwidth selection of the recursive kernel density estimators with missing data in the context of global and local density estimation.
Slaoui, Yousri
core   +3 more sources

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, Anne Daykin, Alison R. G. Shaw, Athene J. Lane, Jane M. Blazeby, Mike Clarke, Paula Williamson, Carrol Gamble   +7 more
doaj   +1 more source