Big data has become a core technology to provide innovative solutions in many fields.
However, the collected dataset for data analysis in various domains will contain missing …

The problem of missing data is common in practice. Many imputation methods have been
developed to fill in the missing entries. However, not all of them can scale to high …

The imputation method should be able to reproduce the actual values in the data or
Predictive Accuracy (PAC) and maintaining the distribution of these values or Distributional …

In data analytics, missing data is a factor that degrades performance. Incorrect imputation of
missing values could lead to a wrong prediction. In this era of big data, when a massive …

The missing data is likely to occur in statistical analyses. The quality of the data is affected by
the used imputation method. In this paper, a method is proposed to impute the missing data …

Many data mining algorithms cannot handle incomplete datasets where some data samples
are missing attribute values. To solve this problem, missing value imputation is usually …

Missing data is a common problem in datasets and impacts the reliability of data analysis.
Numerous methods to impute (ie, predict and replace) missing values have been proposed …

A certain degree of data loss seriously affects the accuracy and availability of data,
especially on the effects of the subsequent in-depth data analysis and mining. It is of great …

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 …

Missing value imputation (MVI) is the major solution method for dealing with incomplete
dataset problems in which the missing attribute values are replaced from a chosen set of …