Results 21 to 30 of about 313,820 (280)

Multi-metric comparison of machine learning imputation methods with application to breast cancer survival [PDF]

BMC Medical Research Methodology
Handling missing data in clinical prognostic studies is an essential yet challenging task. This study aimed to provide a comprehensive assessment of the effectiveness and reliability of different machine learning (ML) imputation methods across various ...
Imad El Badisy, Nathalie Graffeo, Mohamed Khalis, Roch Giorgi   +3 more
doaj   +2 more sources

Missing Data and Imputation Methods. [PDF]

Anesth Analg, 2020
Schober P, Vetter TR.
europepmc   +5 more sources

Ensemble Learning for Multi-Label Classification with Unbalanced Classes: A Case Study of a Curing Oven in Glass Wool Production

Mathematics, 2023
The Industrial Internet of Things (IIoT), which integrates sensors into the manufacturing system, provides new paradigms and technologies to industry.
Minh Hung Ho, Amélie Ponchet Durupt, Hai Canh Vu, Nassim Boudaoud, Arnaud Caracciolo, Sophie Sieg-Zieba, Yun Xu, Patrick Leduc   +7 more
doaj   +1 more source

A Bayesian Approach for Imputation of Censored Survival Data

Stats, 2022
A common feature of much survival data is censoring due to incompletely observed lifetimes. Survival analysis methods and models have been designed to take account of this and provide appropriate relevant summaries, such as the Kaplan–Meier plot and the ...
Shirin Moghaddam, John Newell, John Hinde   +2 more
doaj   +1 more source

A Systematic Literature Review On Missing Values: Research Trends, Datasets, Methods and Frameworks [PDF]

E3S Web of Conferences, 2023
Handling of missing values in data analysis is the focus of attention in various research fields. Imputation is one method that is commonly used to overcome this problem of missing data.
Setiawan Ismail, Gernowo Rahmat, Warsito Budi   +2 more
doaj   +1 more source

A Pragmatic Ensemble Strategy for Missing Values Imputation in Health Records

Entropy, 2022
Pristine and trustworthy data are required for efficient computer modelling for medical decision-making, yet data in medical care is frequently missing.
Shivani Batra, Rohan Khurana, Mohammad Zubair Khan, Wadii Boulila, Anis Koubaa, Prakash Srivastava   +5 more
doaj   +1 more source

Methods to Handle Incomplete Data

MAMC Journal of Medical Sciences, 2020
Context: The major question for data analysis is determining the appropriate analytic approach in the presence of incomplete observations. The most common solution to handle missing data in a data set is imputation, where missing values are estimated and
Vinny Johny, Mariamma Philip, Swathi Augustine   +2 more
doaj   +1 more source

Assessment of genotype imputation methods [PDF]

BMC Proceedings, 2009
Abstract Several methods have been proposed to impute genotypes at untyped markers using observed genotypes and genetic data from a reference panel. We used the Genetic Analysis Workshop 16 rheumatoid arthritis case-control dataset to compare the performance of four of these imputation methods: IMPUTE, MACH, PLINK, and fastPHASE.
Biernacka, Joanna M, Tang, Rui, Li, Jia, McDonnell, Shannon K, Rabe, Kari G, Sinnwell, Jason P, Rider, David N, de Andrade, Mariza, Goode, Ellen L, Fridley, Brooke L   +9 more
openaire   +2 more sources

TIME SERIES IMPUTATION USING VAR-IM (CASE STUDY: WEATHER DATA IN METEOROLOGICAL STATION OF CITEKO)

Barekeng, 2022
Univariate imputation methods are defined as imputation methods that only use the information of the target variable to estimate missing values.
Muhammad Edy Rizal, Aji H Wigena, Farit M Afendi   +2 more
doaj   +1 more source

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