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 …

This paper studies the classification of unbalanced data sets. First, this kind of data sets is
briefly introduced, and then the classification methods of unbalanced data sets are analyzed …

The COVID-19 pandemic has limited people's mobility to a certain extent, making it difficult
to purchase goods and services offline, which has led the creation of a culture of increased …

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 real-time acquisition of surrounding rock information is important for the efficient
tunneling and hazard prevention in tunnel boring machines (TBMs). This study presents an …

Training classifiers with datasets which suffer of imbalanced class distributions is an
important problem in data mining. This issue occurs when the number of examples …

Abstract Machine learning classifiers perform well on balanced datasets. Unfortunately, a lot
of the real-world data sets are naturally imbalanced. So, imbalanced classification is a …