Abstract Machine Learning (ML) algorithms have been increasingly replacing people in
several application domains—in which the majority suffer from data imbalance. In order to …

Class imbalance is often faced by real-world datasets where one class contains a smaller
number of instances than the other one. Even though this has been an area of interest for …

Evolutionarily conserved signaling pathways are essential for early embryogenesis, and
reducing or abolishing their activity leads to characteristic developmental defects …

Imbalanced classification on bankruptcy prediction is considered as one of the most
important topics in financial institutions. In this context, various statistical and artificial …

Many binary class datasets in real-life applications are affected by class imbalance problem.
Data complexities like noise examples, class overlap and small disjuncts problems are …

Higher education institutions record a significant amount of data about their students,
representing a considerable potential to generate information, knowledge, and monitoring …

Machine unlearning using the SISA technique promises a significant speedup in model
retraining with only minor sacrifices in performance. Even greater speedups can be …

Epilepsy is one of the brightest manifestations of extreme behavior in living systems.
Extreme epileptic events are seizures, that arise suddenly and unpredictably. Usually …

Introduction The prevalence of end-stage renal disease has raised the need for renal
replacement therapy over recent decades. Even though a kidney transplant offers an …

Resampling, one of the approaches to handle class imbalance, is widely used alone or in
combination with other approaches, such as cost-sensitive learning and ensemble learning …