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 …

Objective Methods to correct class imbalance (imbalance between the frequency of outcome
events and nonevents) are receiving increasing interest for developing prediction models …

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 …

Supervised learning techniques are widely employed in credit card fraud detection, as they
make use of the assumption that fraudulent patterns can be learned from an analysis of past …

Detecting frauds in credit card transactions is perhaps one of the best testbeds for
computational intelligence algorithms. In fact, this problem involves a number of relevant …

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 …

Under sampling is a popular technique for unbalanced datasets to reduce the skew in class
distributions. However, it is well-known that under sampling one class modifies the priors of …

The expansion of the electronic commerce, together with an increasing confidence of
customers in electronic payments, makes of fraud detection a critical factor. Detecting frauds …

We analyze the performance of a set of machine learning models in predicting default risk,
using standard statistical models, such as the logistic regression, as a benchmark. When …