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 …

Abstract The Synthetic Minority Over-sampling Technique (SMOTE) is a well-known
resampling strategy that has been successfully used for dealing with the class-imbalance …

With the rapid advancement of big data technology, high-dimensional datasets comprising
multi-label data have become prevalent in various fields. However, these datasets often …

The class imbalance problem is characterized by an unequal data distribution in which
majority classes have a greater number of data samples than minority classes …

In recent years, the means of disease diagnosis and treatment have been improved
remarkably, along with the continuous development of technology and science …

Abstract Integrating Machine Learning (ML) in medicine has unlocked many opportunities to
harness complex medical data, enhancing patient outcomes and advancing the field …

Class imbalance is a common issue in the community of machine learning and data mining.
The class-imbalance distribution can make most classical classification algorithms neglect …

The student academic performance prediction is becoming an indispensable service in the
computer supported intelligent education system. But conventional machine learning-based …

Chest X-rays (CXR) are essential in the diagnosis of lung disease, but CXR image
classification is challenging because patients often have multiple diseases simultaneously …

Multi-label learning tasks usually encounter the problem of the class-imbalance, where
samples and their corresponding labels are non-uniformly distributed over multi-label data …