In this paper, we propose a conditional variational autoencoder-based self-transferred
(CVAE_SeTred) algorithm to solve the highly imbalanced classification problem, where the …

Class imbalance is a prevalent phenomenon in various real-world applications and it
presents significant challenges to model learning, including deep learning. In this work, we …

Resampling is the widely used method for imbalance learning. Most existing resampling
methods use various techniques in the original sample space to rebalance imbalanced …

In recent years, most researchers focused on the classification problems of imbalanced data
sets, and these problems are widely distributed in industrial production and medical …

Many classification problems encountered in real-world applications exhibit a profile of
imbalanced data. Current methods depend on data resampling. In fact, if the feature set …

Class imbalances have been reported to compromise the performance of most standard
classifiers, such as Naive Bayes, Decision Trees and Neural Networks. Aiming to solve this …

It is hard to construct an optimal classifier for high-dimensional imbalanced data, on which
the performance of classifiers is seriously affected and becomes poor. Although many …

Class imbalance is an active research area in the machine learning community. However,
existing and recent literature showed that class overlap had a higher negative impact on the …

The dynamic ensemble selection of classifiers is an effective approach for processing label-
imbalanced data classifications. However, such a technique is prone to overfitting, owing to …

Learning from a multi-class problem has not been an easy task for most of the classifiers,
because of multiple issues. In the complex multi-class scenarios, samples of different …