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 …

Learning from class imbalanced datasets poses challenges for many machine learning
algorithms. Many real-world domains are, by definition, class imbalanced by virtue of having …

Since most classifiers are biased toward the dominant class, class imbalance is a
challenging problem in machine learning. The most popular approaches to solving this …

Class imbalance occurs in classification problems in which the “normal” cases, or instances,
significantly outnumber the “abnormal” instances. Training a standard classifier on …

Oversampling is a promising preprocessing technique for imbalanced datasets which
generates new minority instances to balance the dataset. However, improper generated …

This paper proposes a simple interpolation Oversampling method with the purpose of
Reliably Expanding the Minority class regions (OREM). OREM first finds the candidate …

Data imbalance remains one of the factors negatively affecting the performance of
contemporary machine learning algorithms. One of the most common approaches to …

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 …

The world is constantly changing, and so are the massive amount of data produced.
However, only a few studies deal with online class imbalance learning that combines the …

Deep learning models may not effectively generalize across under-represented or minority
classes. We empirically study a convolutional neural network's (CNN) internal …