Learning with imbalanced data refers to the scenario in which the amounts of instances that
represent the concepts in a given problem follow a different distribution. The main issue …

imbalanced-learn is an open-source python toolbox aiming at providing a wide range of
methods to cope with the problem of imbalanced dataset frequently encountered in machine …

Billions of dollars of loss are caused every year due to fraudulent credit card transactions.
The design of efficient fraud detection algorithms is key for reducing these losses, and more …

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 …

Intrusion detection (ID) on the cloud environment has received paramount interest over the
last few years. Among the latest approaches, machine learning-based ID methods allow us …

A well-known rule of thumb in unbalanced classification recommends the rebalancing
(typically by resampling) of the classes before proceeding with the learning of the classifier …

Addressing imbalanced datasets in classification tasks is a relevant topic in research
studies. The main reason is that for standard classification algorithms, the success rate when …

The number of daily credit card transactions is inexorably growing: the e-commerce market
expansion and the recent constraints for the Covid-19 pandemic have significantly …

Social media platforms allow billions of individuals to share their thoughts, likes and dislikes
in real-time, without any censorship. This freedom, however, comes at a cyber-security risk …

Abstract Background and Objective Clinical concern for acute coronary syndrome (ACS) is
one of emergency medicine's most common patient encounters. This study aims to develop …