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

Despite more than two decades of continuous development learning from imbalanced data
is still a focus of intense research. Starting as a problem of skewed distributions of binary …

Learning and mining from imbalanced datasets gained increased interest in recent years.
One simple but efficient way to increase the performance of standard machine learning …

For the last two decades, oversampling has been employed to overcome the challenge of
learning from imbalanced datasets. Many approaches to solving this challenge have been …

Model learning from class imbalanced training data is a long-standing and significant
challenge for machine learning. In particular, existing deep learning methods consider …

Rare event prediction involves identifying and forecasting events with a low probability using
machine learning (ML) and data analysis. Due to the imbalanced data distributions, where …

Imbalanced class data distribution occurs when the number of examples representing one
class is much lower than others. This conditioning affects the prediction accuracy degraded …

There have been many attempts to classify imbalanced data, since this classification is
critical in a wide variety of applications related to the detection of anomalies, failures, and …

In medical diagnosis, eg bowel cancer detection, a large number of examples of normal
cases exists with a much smaller number of positive cases. Such data imbalance usually …

Loom malfunctions are the main cause of faulty fabric production. A fabric inspection system
is a specialized computer vision system used to detect fabric defects for quality assurance. In …