The imbalanced learning issue is related to the performance of learning algorithms in the
presence of asymmetrical class distribution. Due to the complex characteristics of …

The theoretical developments of data-driven fault diagnosis methods have yielded fruitful
achievements and significantly benefited industry practices. However, most methods are …

The purpose of this study is to examine existing deep learning techniques for addressing
class imbalanced data. Effective classification with imbalanced data is an important area of …

Learning from class-imbalanced data continues to be a common and challenging problem in
supervised learning as standard classification algorithms are designed to handle balanced …

Machine learning classifiers trained on class imbalanced data are prone to overpredict the
majority class. This leads to a larger misclassification rate for the minority class, which in …

Many real-world data-mining applications involve obtaining predictive models using
datasets with strongly imbalanced distributions of the target variable. Frequently, the least …

The primary objective of implementing Electronic Health Records (EHRs) is to improve the
management of patients' health-related information. However, these records have also been …

Abstract Predictive performance of Machine Learning (ML) models rely on the quality of data
used for training the models. However, if the training data is not balanced among different …

Training classifiers with datasets which suffer of imbalanced class distributions is an
important problem in data mining. This issue occurs when the number of examples …

Imbalanced learning problems contain an unequal distribution of data samples among
different classes and pose a challenge to any classifier as it becomes hard to learn the …