Current research on imbalanced data recognises that class imbalance is aggravated by
other data intrinsic characteristics, among which class overlap stands out as one of the most …

Class imbalance is an active research area in the machine learning community. However,
existing and recent literature showed that class overlap had a higher negative impact on the …

Class imbalance with overlap is a very challenging problem in electronic fraud transaction
detection. Fraudsters have racked their brains to make a fraud transaction as similar as a …

Abstract Software Defect Prediction (SDP) is highly crucial task in software development
process to forecast about which modules are more prone to errors and faults before the …

Class imbalanced datasets are common across different domains including health, security,
banking and others. A typical supervised learning algorithm tends to be biased towards the …

The combination of class imbalance and overlap is currently one of the most challenging
issues in machine learning. While seminal work focused on establishing class overlap as a …

The class-imbalanced classification is a difficult problem because not only traditional
classifiers are more biased towards the majority classes and inclined to generate incorrect …

Standard classification algorithms assume the class distribution of data to be roughly
balanced. Class imbalance problem usually occurs in real-life applications, such as direct …

Imbalanced dataset classification issue poses a major challenge on machine learning
domain. Traditional supervised learning algorithms usually bias towards the majority class …

A fluent conversation with a virtual assistant, person-tailored news feeds, and deep-fake
images created within seconds—all those things that have been unthinkable for a long time …