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 …

Missing value imputation (MVI) has been studied for several decades being the basic
solution method for incomplete dataset problems, specifically those where some data …

k nearest neighbor (kNN) method is a popular classification method in data mining and
statistics because of its simple implementation and significant classification performance …

The K Nearest Neighbor (kNN) method has widely been used in the applications of data
mining and machine learning due to its simple implementation and distinguished …

Our research work describes a team of human Digital Twins (DTs), each tracking fitness-
related measurements describing an athlete's behavior in consecutive days (eg food …

This paper studies an example-driven k-parameter computation that identifies different k
values for different test samples in kNN prediction applications, such as classification …

Existing kNN imputation methods for dealing with missing data are designed according to
Minkowski distance or its variants, and have been shown to be generally efficient for …

Missing data imputation is a key issue in learning from incomplete data. Various techniques
have been developed with great successes on dealing with missing values in data sets with …

Accurate prediction in the presence of large number of missing values in the data set has
always been a challenging problem. Most of hybrid models to address this challenge have …

Both subspace learning methods and feature selection methods are often used for removing
irrelative features from high-dimensional data. Studies have shown that feature selection …