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

Many real-world data-mining applications involve obtaining predictive models using
datasets with strongly imbalanced distributions of the target variable. Frequently, the least …

Species distribution modeling (SDM) is widely used in ecology and conservation. Currently,
the most available data for SDM are species presence‐only records (available through …

Deep learning has recently unleashed the ability for Machine learning (ML) to make
unparalleled strides. It did so by confronting and successfully addressing, at least to a …

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 …

Machine learning classifiers trained on class imbalanced data are prone to overpredict the
majority class. This leads to a larger misclassification rate for the minority class, which in …

The theoretical developments of data-driven fault diagnosis methods have yielded fruitful
achievements and significantly benefited industry practices. However, most methods are …

The random forest (RF) algorithm is an ensemble of classification or regression trees and is
widely used, including for species distribution modelling (SDM). Many researchers use …

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 …