Managing missing data is a decisive work to ensure good results in mining. In order to get
the complete knowledge of dataset, the imputation technique is required to fill the missing …

The performance of the classification algorithm depends on the quality of the training data.
Data quality is an important factor that affects the data mining classification results. However …

Incomplete data or missing data diminishes the effectivity of statistical results, and may
cause bias estimates, which in turn leads to unsound judgment. Inefficiency and …

Data is an important asset for any organization to successfully run its business. When we
collect data, it contains data with low qualities such as noise, incomplete, missing values etc …

Abstract The University of California Irvine Heart disease dataset had missing data on
several attributes. The missing data can loss the important information of the attributes, but it …

Abstract Machine learning has been the corner stone in analysing and extracting information
from data and often a problem of missing values is encountered. Missing values occur …

The quality of data is an important task in the data mining. The validity of mining algorithms
is reduced if data is not of good quality. The quality of data can be assessed in terms of …

Missing values create issues during the analysis of the dataset. Learning algorithms in an
asymmetrical dataset can generate an overrated classification accuracy due to a bias …

Real world data often contain missing values. Data mining techniques have been actively
used to overcome this problem by using methods of imputing the missing values. In …

Missing value is a widespread problem for data quality because most of the statistical
procedures require a value for each variable. The missing value may lead to biased …