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 …

Background and objective Medical image classification is often challenging for two reasons:
a lack of labelled examples due to expensive and time-consuming annotation protocols, and …

Network intrusion detection systems (NIDSs) play an important role in computer network
security. There are several detection mechanisms where anomaly-based automated …

Node classification is an important research topic in graph learning. Graph neural networks
(GNNs) have achieved state-of-the-art performance of node classification. However, existing …

Data augmentation has been widely used to improve generalizability of machine learning
models. However, comparatively little work studies data augmentation for graphs. This is …

Skin lesion classification plays a crucial role in diagnosing various gene and related local
medical cases in the field of dermoscopy. In this paper, a new model for the classification of …

We analyze the predictability of the bitcoin market across prediction horizons ranging from 1
to 60 min. In doing so, we test various machine learning models and find that, while all …

Data augmentation has recently seen increased interest in graph machine learning given its
demonstrated ability to improve model performance and generalization by added training …

This paper presents a novel approach to deal with the imbalanced data set problem in
neural networks by incorporating prior probabilities into a cost-sensitive cross-entropy error …

The class imbalance problems have been reported to severely hinder classification
performance of many standard learning algorithms, and have attracted a great deal of …