Classification of imbalanced data is a vastly explored issue of the last and present decade
and still keeps the same importance because data are an essential term today and it …

Nowadays, in the international scientific community of machine learning, there exists an
enormous discussion about the use of black-box models or explainable models; especially …

Translating data to knowledge and actionable insights is the Holy Grail for many scientific
fields, including biology. The unprecedented massive and heterogeneous data have created …

Data in vision domain often exhibit highly-skewed class distribution, ie, most data belong to
a few majority classes, while the minority classes only contain a scarce amount of instances …

Learning and mining from imbalanced datasets gained increased interest in recent years.
One simple but efficient way to increase the performance of standard machine learning …

The class imbalance problems have been reported to severely hinder classification
performance of many standard learning algorithms, and have attracted a great deal of …

Imbalanced Data (ID) is a problem that deters Machine Learning (ML) models from
achieving satisfactory results. ID is the occurrence of a situation where the quantity of the …

Anomaly detection in energy consumption is a crucial step towards developing efficient
energy saving systems, diminishing overall energy expenditure and reducing carbon …

Subpopulation shift widely exists in many real-world machine learning applications, referring
to the training and test distributions containing the same subpopulation groups but varying in …

Melanoma is the deadliest type of skin cancer with highest mortality rate. However, the
annihilation in early stage implies a high survival rate therefore, it demands early diagnosis …