The problem of class imbalance has always been considered as a significant challenge to
traditional machine learning and the emerging deep learning research communities. A …

Several oversampling methods have been proposed for solving the class imbalance
problem. However, most of them require searching the k-nearest neighbors to generate …

Identifying unseen classes with limited labeled data for reference is a challenging task,
which is also known as few-shot learning. Generally, a knowledge-rich model is more robust …

Abstract Cost-sensitive learning (CSL), which has gained widespread attention in class
imbalance learning (CIL), can be implemented either by tuning penalty parameters or by …

The theory of fuzzy rough sets is an effective soft computing paradigm for dealing with
vague, uncertain, or imprecise data. However, most existing fuzzy rough sets-based …

Abstract Few-Shot Event Classification (FSEC) aims at assigning event labels to unlabeled
sentences when limited annotated samples are available. Existing works mainly focus on …

Although identifying high-cost inpatients with ischemic heart disease (IHD) at the point of
admission is helpful for timely intervention and reducing costs, it is a difficult task due to the …

Background Structural changes in psychiatric systems have altered treatment opportunities
for patients in need of mental healthcare. These changes are possibly associated with an …

Unequal data distribution among different classes usually cause a class imbalance problem.
Due to the class imbalance, the classification models become biased toward the majority …

Predicting hospital readmission with effective machine learning techniques has attracted a
great attention in recent years. The fundamental challenge of this task stems from …