Missing data is a problem often found in real-world datasets and it can degrade the
performance of most machine learning models. Several deep learning techniques have …

Missing data are a universal data quality problem in many domains, leading to misleading
analysis and inaccurate decisions. Much research has been done to investigate the different …

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

Abstract ydata-profiling is an open-source Python package for advanced exploratory data
analysis that enables users to generate data profiling reports in a simple, fast, and efficient …

In almost all countries, precautionary measures are less expensive than medical treatment.
The early detection of any disease gives a patient better chances of successful treatment …

This work performs an in-depth study of the impact of distance functions on K-Nearest
Neighbours imputation of heterogeneous datasets. Missing data is generated at several …

Missing data consists in the lack of information in a dataset and since it directly influences
classification performance, neglecting it is not a valid option. Over the years, several studies …

In missing data contexts, k-nearest neighbours imputation has proven beneficial since it
takes advantage of the similarity between patterns to replace missing values. When dealing …

Longitudinal datasets of human ageing studies usually have a high volume of missing data,
and one way to handle missing values in a dataset is to replace them with estimations …

The integrative analysis of complementary phenotype information contained in multi-
modality data (eg, histopathological images and genomic data) has advanced the …