Results 251 to 260 of about 318,466 (281)

GBKII: An Imputation Method for Missing Values

2007
Missing data imputation is an actual and challenging issue in machine learning and data mining. This is because missing values in a dataset can generate bias that affects the quality of the learned patterns or the classification performances. To deal with this issue, this paper proposes a Grey-Based K-NN Iteration Imputation method, called GBKII, for ...
Chengqi Zhang, Xiaofeng Zhu 0001, Jilian Zhang, Yongsong Qin, Shichao Zhang 0001   +4 more
openaire   +1 more source

Imputation methods for incomplete data

2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), 2015
Although sometimes encounter data sets that contain one or more missing feature values (incomplete data). Many existing industrial and research data sets contain missing values due to various reasons, such as manual data entry procedures, equipment errors and incorrect measurements.
Vaishali H. Umathe, Gauri Chaudhary
openaire   +1 more source

Bootstrap methods for imputed data from regression, ratio and hot‐deck imputation

Canadian Journal of Statistics, 2014
AbstractItem non‐response in sample surveys is usually addressed by imputation. A bootstrap method that treats the imputed values as if they were observed generally leads to variance estimates that are too small. Shao & Sitter (1996) introduced a bootstrap method in this context, which leads to consistent variance estimators when the sampling ...
Mashreghi, Zeinab, Léger, Christian, Haziza, David   +2 more
openaire   +2 more sources

Deep learning versus conventional methods for missing data imputation: A review and comparative study

Expert Systems With Applications, 2023
Jing Li, TingTing Zhang, Xiaofeng Wang   +2 more
exaly  

Comparison of imputation and imputation-free methods for statistical analysis of mass spectrometry data with missing data

Briefings in Bioinformatics, 2022
Sandra L Taylor, Machelle D Wilson, Kyoungmi Kim   +2 more
exaly  

Imputation Methods for Single Variables

2018
This chapter considers imputation methods for single variables. Naturally, it may be necessary to impute the values of several variables in each dataset and to carry out several imputations for each dataset. It is essential to understand the basics of Chap. 11, which presents the starting point for imputation methods.
openaire   +1 more source

Imputation Method for Multidimensional Data

2023 3rd International Conference on Intelligent Technologies (CONIT), 2023
Jay Naik, Anil Jadhav
openaire   +1 more source

Multiple imputation using nearest neighbor methods

Information Sciences, 2021
Shahla Faisal, Gerhard Tutz
exaly  

Some improved and alternative imputation methods for finite population mean in presence of missing information

Communications in Statistics - Theory and Methods, 2021
G N Singh, Awadhesh K Pandey, Anup Kumar Sharma   +2 more
exaly  

Improved Methods of Imputation for Clustering

2023 9th International Conference on Smart Computing and Communications (ICSCC), 2023
E V Veena, K P Pushpalatha
openaire   +1 more source