Imbalanced learning constitutes one of the most formidable challenges within data mining
and machine learning. Despite continuous research advancement over the past decades …

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 …

This paper proposes a novel adversarial scheme for learning from data under harsh
learning conditions of partially labelled samples and skewed class distributions. This novel …

The bi-directional flow of energy and information in the smart grid makes it possible to record
and analyze the electricity consumption profiles of consumers. Because of the increasing …

Many real-world datasets exhibit imbalanced distributions, in which the majority classes
have sufficient samples, whereas the minority classes often have a very small number of …

The Air Quality Index (AQI) dataset contains information on measurements of pollutants and
ambient air quality conditions at certain location that can be used to predict air quality …

In data mining classification, class imbalance is characterized that different classes have an
obvious difference in the number of samples. Most classifiers typically assume a balanced …

In real cases, missing values tend to contain meaningful information that should be acquired
or should be analyzed before the incomplete dataset is used for machine learning tasks. In …

Imbalanced classification is widely referred in many real-world applications and has been
extensively studied. Most existing algorithms consider alleviating the imbalance by sampling …

The continuous malicious software (malware) attacks on smartphones pose a serious threat
to the security of users, especially the dominant platform Android. Data-driven methods …