Results 221 to 230 of about 161,037 (263)

Imputation of Missing Data in Industrial Databases

Applied Intelligence, 1999
A limiting factor for the application of IDA methods in many domains is the incompleteness of data repositories. Many records have fields that are not filled in, especially, when data entry is manual. In addition, a significant fraction of the entries can be erroneous and there may be no alternative but to discard these records.
Kamakshi Lakshminarayan, Steven A. Harp, Tariq Samad   +2 more
openaire   +1 more source

Missing Data Imputation

2012
Missing data in clinical research data is often a real problem. As an example, a 35 patient data file of 3 variables consists of 3 × 35 = 105 values if the data are complete. With only 5 values missing (1 value missing per patient) 5 patients will not have complete data, and are rather useless for the analysis.
Ton J. Cleophas, Aeilko H. Zwinderman
openaire   +1 more source

Imputation of the Missing Data

2013
We may consider the existence of missing observations as unimportant, considering that the risk of misunderstanding is negligible. The surveyor assumes some model that allows adequately explaining the variable of interest. In such cases, we are able to predict the unknown values and to plug them into some estimator.
openaire   +1 more source

[Imputation of missing data].

Nederlands tijdschrift voor geneeskunde, 2013
In medical research missing data are sometimes inevitable. Different missingness mechanisms can be distinguished: (a) missing completely at random; (b) missing by design; (c) missing at random, and (d) missing not at random. If participants with missing data are excluded from statistical analyses, this can lead to biased study results and loss of ...
Ralph C A, Rippe, Martin, den Heijer, Saskia, le Cessie   +2 more
openaire   +1 more source

Missing Data Imputation for Machine Learning

2019
The imputation of missing values in datasets always plays an important role in the data preprocessing. In the process of data collection, because of the various reasons, the datasets often contain some missing values, and the excellent missing data imputation algorithms can increase the reliability of the dataset and reduce the impact of missing values
Shaoqian Wang, Bo Li 0090, Mao Yang 0001, Zhongjiang Yan   +3 more
openaire   +1 more source

Imputation of Missing Ages in Pedigree Data

Human Heredity, 2007
<i>Background:</i> In human pedigree data age at disease occurrence frequently is missing and is imputed using various methods. However, little is known about the performance of these methods when applied to families. In particular, there is little information about the level of agreement between imputed and actual values of temporal data ...
Raymond R, Balise, Yu, Chen, Gillian, Dite, Anna, Felberg, Limei, Sun, Argyrios, Ziogas, Alice S, Whittemore   +6 more
openaire   +2 more sources

Optimized Parameters for Missing Data Imputation

2006
To complete missing values, a solution is to use attribute correlations within data. However, it is difficult to identify such relations within data containing missing values. Accordingly, we develop a kernel-based missing data imputation method in this paper.
Shichao Zhang 0001, Yongsong Qin, Xiaofeng Zhu 0001, Jilian Zhang, Chengqi Zhang   +4 more
openaire   +1 more source

Meta-GAIN for Missing Data Imputation

Proceedings of the AAAI Conference on Artificial Intelligence
Although previous deep imputation methods (eg., Generative Adversarial Network (GAN) based methods) have been widely designed to impute missing data, they still suffer from the issues, ie., lack of the imputation diversity and the generalization ability.
Tao Tong, Xiaofeng Zhu 0001, Jiangzhang Gan   +2 more
openaire   +1 more source

Influence of Data Distribution in Missing Data Imputation

2017
Dealing with missing data is a crucial step in the preprocessing stage of most data mining projects. Especially in healthcare contexts, addressing this issue is fundamental, since it may result in keeping or loosing critical patient information that can help physicians in their daily clinical practice.
Miriam Seoane Santos, Jastin Pompeu Soares, Pedro Henriques Abreu, Hélder Araújo, João A. M. Santos   +4 more
openaire   +1 more source

IMPUTING MISSING DATA

Journal of the American Academy of Child & Adolescent Psychiatry, 2004
Calvin D, Croy, Douglas K, Novins
openaire   +2 more sources