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 …

The combination of class imbalance and overlap is currently one of the most challenging
issues in machine learning. While seminal work focused on establishing class overlap as a …

In recent years, the classification of class-imbalanced data has obtained increasing attention
across different scientific areas such as fraud detection, metabolomics, Cancer diagnosis …

In data mining classification, class imbalance is characterized that different classes have an
obvious difference in the number of samples. Most classifiers typically assume a balanced …

Nowadays, machine learning techniques are employed in a wide range of applications,
where classification is a common task in machine learning. It predicts the class label of a …

The traditional systems used in the physiotherapy rehabilitation process are evolving
towards more advanced systems that use virtual reality (VR) environments so that the patient …

Learning from class-imbalanced data has gained increasing attention in recent years due to
the massive growth of skewed data across many scientific fields such as metabolomics …

This paper proposes a distance-based index to evaluate the input data and the data flowing
through layers of a convolutional neural network in classification problems. This shape-less …

Data imbalance is a challenging problem in classification tasks, and when combined with
class overlapping, it further deteriorates classification performance. However, existing …

Data are the critical fuel for Artificial Intelligence (AI) models. Poor quality data produces
inaccurate and ineffective AI models that may lead to incorrect or unsafe use. Checking for …