The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is
considered" de facto" standard in the framework of learning from imbalanced data. This is …

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 …

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 class-imbalance problem is an important area that plagues machine learning and data
mining researchers. It is ubiquitous in all areas of the real world. At present, many methods …

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 classification in class imbalanced data has drawn significant interest in medical
application. Most existing methods are prone to categorize the samples into the majority …

Learning from imbalanced data, where the number of observations in one class is
significantly rarer than in other classes, has gained considerable attention in the data mining …

This paper proposes an ensemble algorithm named of BPSO-Adaboost-KNN to cope with
multi-class imbalanced data classification. The main idea of this algorithm is to integrate …