Given a set of observations that each belong to a certain class, supervised classification
aims to learn a classification model that can predict the class of a new, unlabeled …

Class imbalance a common challenge in machine learning, often results in skewed
predictions and misrepresentative model assessments, highlighting the need for effective …

When facing a classification problem, data science practitioners must search through an
armory of methods. Often, practitioners are tempted to use off-the-shelf classifiers, including …

The class imbalance problem is a pervasive issue in many real-world domains.
Oversampling methods that inflate the rare class by generating synthetic data are amongst …

The class imbalance problem is a pervasive issue in many real-world domains.
Oversampling methods that inflate the rare class by generating synthetic data are amongst …

In data science and machine learning, imbalanced data poses a significant challenge. This
study presents a self-balancing strategy integrating traditional (randomly duplicating data …

When working with real-world applications we often find imbalanced datasets, those for
which there exists a majority class with normal data and a minority class with abnormal or …

Poorly separated data and imbalanced data present major challenges to classifiers which
often result in lower accuracy and reliability in making predictions. In this paper, we …

Class imbalance is characterized by an uneven distribution of classes, resulting in
significantly more instances in one class (majority class or negative class) than in the others …

Many real-world classification problems are significantly class-imbalanced to detriment of
the class of interest. The standard set of proper evaluation metrics is well-known but the …