This survey paper focuses on one of the current primary issues challenging data mining
researchers experimenting on real‐world datasets. The problem is that of imbalanced class …

In an actual industrial scenario, machines typically operate normally for the majority of the
time, with malfunctions occurring only occasionally. As a result, there is very little recorded …

The generation of synthetic data can be used for anonymization, regularization,
oversampling, semi-supervised learning, self-supervised learning, and several other tasks …

Context: Generally, there are more non-defective instances than defective instances in the
datasets used for software defect prediction (SDP), which is referred to as the class …

Broad learning system (BLS) is a novel and efficient model, which facilitates representation
learning and classification by concatenating feature nodes and enhancement nodes. In spite …

The imbalanced data classification problem widely exists in many real-world applications.
Data resampling is a promising technique to deal with imbalanced data through either …

Class imbalance is a critical issue in customer classification, for which a plethora of
techniques have been proposed in the current body of literature. In particular, generative …

In pattern classification, the class imbalance problem always occurs when the number of
observations in some classes is significantly different from that of other categories, which …

Although deep learning exhibits advantages in various applications involving multimodal
data, it cannot effectively solve the class-imbalance problem. Herein, we propose a hybrid …

Brain-computer interfaces (BCIs) allow users to communicate directly with external devices
via their brain signals. Recently, BCIs, and wearable computers in particular, have been …