Results 221 to 230 of about 131,123 (260)

A hybrid method for missing value imputation

Proceedings of the 23rd Pan-Hellenic Conference on Informatics, 2019
Missing values are a common incurrence in a great number of real-world datasets, emerging from diverse domains of interest. In research, missing data constitute a significant problem as it can affect the conclusions drawn from them. Considering this, the difficulty of data preprocessing is increasing as selecting an inappropriate way to handle missing ...
Aikaterini Karanikola, Sotiris Kotsiantis   +1 more
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Semantic Technologies Towards Missing Values Imputation

2021
Missing values are a data quality problem affecting almost every type of real world datasets. Since poor data quality has a direct impact on organisational success, there is a dire need to eradicate missing values as a way to minimise costs and increase efficiency in companies.
Iker Esnaola-Gonzalez, Unai Garciarena, Jesús Bermúdez   +2 more
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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
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Order-Sensitive Imputation for Clustered Missing Values

IEEE Transactions on Knowledge and Data Engineering, 2019
The issue of missing values (MVs) has appeared widely in real-world datasets and hindered the use of many statistical or machine learning algorithms for data analytics due to their incompetence in handling incomplete datasets. To address this issue, several MV imputation algorithms have been developed. However, these approaches do not perform well when
Qian Ma 0003, Yu Gu 0002, Wang-Chien Lee, Ge Yu 0001   +3 more
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Imputation of Missing Values through Profiling Metadata. [PDF]

, 2022
Among the several problems related to the management of database instances, missing values represents a crucial factor that could severely compromise the integrity and the meaningfulness of such data representations. Thus, the data imputation research field focuses its efforts on solutions for filling missing values by means of plausible candidates ...
Breve B., Caruccio L., Deufemia V., Polese G.   +3 more
openaire   +2 more sources

Imputing missing values for genetic interaction data

Methods, 2014
Epistatic Miniarray Profiles (EMAP) enable the research of genetic interaction as an important method to construct large-scale genetic interaction networks. However, a high proportion of missing values frequently poses problems in EMAP data analysis since such missing values hinder downstream analysis.
Yishu, Wang, Lin, Wang, Dejie, Yang, Minghua, Deng   +3 more
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Triple imputation for microarray missing value estimation

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2015
Data obtained from gene expression microarray experiments always suffer from missing values due to various reasons. However, complete gene expression data are of great importance to many gene expression data analysis issues. Therefore, imputation methods with high estimation precision are critical to further data analysis.
Chong He, Hui-Hui Li, Changbo Zhao, Guo-Zheng Li 0001, Wei Zhang   +4 more
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Imputation for missing values and corresponding variance estimation

Canadian Journal of Statistics, 1997
Summary: Imputation is commonly used to compensate for missing data in surveys. We consider the general case where the responses on either the variable of interest \(y\) or the auxiliary variable \(x\) or both may be missing. We use ratio imputation for \(y\) when the associated \(x\) is observed and different imputations when \(x\) is not observed. We
Sitter, R. R., Rao, J. N. K.
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A Novel Framework for Imputation of Missing Values in Databases

IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 2007
Many of the industrial and research databases are plagued by the problem of missing values. Some evident examples include databases associated with instrument maintenance, medical applications, and surveys. One of the common ways to cope with missing values is to complete their imputation (filling in).
Alireza Farhangfar, Lukasz A. Kurgan, Witold Pedrycz   +2 more
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Missing value imputation: with application to handwriting data

SPIE Proceedings, 2015
Missing values make pattern analysis difficult, particularly with limited available data. In longitudinal research, missing values accumulate, thereby aggravating the problem. Here we consider how to deal with temporal data with missing values in handwriting analysis.
Zhen Xu, Sargur N. Srihari
openaire   +1 more source