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 …

Fifty years have passed since the publication of the first regression tree algorithm. New
techniques have added capabilities that far surpass those of the early methods. Modern …

Big data and analytics (BDA) are gaining momentum, particularly in the practitioner world.
Research linking BDA to improved organizational performance seems scarce and widely …

This chapter addresses fundamental issues of change in psychotherapy: how to measure,
monitor, predict change, and provide feedback on treatment outcome. The chapter starts …

The paper presents the first known longitudinal study of the relationship between loneliness,
depressive symptoms, and suicide ideation in adolescence, in a stratified sample of high …

In real-life data, information is frequently lost in data mining, caused by the presence of
missing values in attributes. Several schemes have been studied to overcome the …

When applying data-mining techniques to real-world data, we often find ourselves facing
observations that have no value recorded for some attributes. This can be caused by several …

Due to the ubiquity of missing data, data imputation has received extensive attention in the
past decades. It is a well-recognized problem impacting almost all fields of scientific study …

The performance evaluation of imputation algorithms often involves the generation of
missing values. Missing values can be inserted in only one feature (univariate configuration) …