An imbalanced dataset consists of a majority class and a minority class, where the former's
sample size is substantially larger than other classes. This difference disrupts the data …

The recent increase in credit card fraud is rapidly has caused huge monetary losses for
individuals and financial institutions. Most credit card frauds are conducted online by …

In the context of machine learning, an imbalanced classification problem states to a dataset
in which the classes are not evenly distributed. This problem commonly occurs when …

It is an enormous challenge for imbalanced data learning in the field of machine learning. To
construct balanced datasets, oversampling techniques have been studied extensively …

Learning classifiers with uneven class distribution datasets poses a significant challenge in
software defect prediction. This problem arises when the number of samples representing …

Oversampling techniques are widely used to rebalance imbalanced datasets. However,
most of the oversampling methods may introduce noise and fuzzy boundaries for dataset …

Highways, as one of the main arteries of transit and transportation in today's world, play a
fundamental role in accelerating transportation, and for this reason, continuous monitoring of …

The expediting magnification of automating the manual jobs into automated is incrementing
day by day, as there are approximately 2.5 quintillion bytes of data exchanged over the …

This study explores the effects of class label noise on detecting fraud within three highly
imbalanced healthcare fraud data sets containing millions of claims and minority class sizes …

Tujuan dari penelitian ini adalah membandingkan efektivitas penggunaan teknik data
seimbang (balance) menggunakan Adaptive Synthetic (ADASYN) dengan metode …