In machine learning, the data imbalance imposes challenges to perform data analytics in
almost all areas of real-world research. The raw primary data often suffers from the skewed …

Rare events, especially those that could potentially negatively impact society, often require
humans' decision-making responses. Detecting rare events can be viewed as a prediction …

With the emergence of ever-advancing network threats, the guarantee of system security
becomes increasingly crucial, especially in the dynamic and decentralized ad-hoc networks …

The data mining and machine learning community is often faced with two key problems:
working with imbalanced data and selecting the best features for machine learning. This …

SMOTE (Synthetic Minority Oversampling TEchnique) is one of the most popular and well-
known sampling algorithms for addressing class imbalance learning problem. The merits of …

With the continuous development of industrial IoT (IIoT) technology, network security is
becoming more and more important. And intrusion detection is an important part of its …

Electronic fraud is highly lucrative, with estimates suggesting these crimes to be worth
millions of dollars annually. Because of its complex nature, electronic fraud detection is …

Learning from imbalanced data is a challenging task in the fields of machine learning and
data mining. As an effective and efficient solution, cost-sensitive learning has been widely …

The KDD Cup'99 data set has been widely used to evaluate intrusion detection prototypes,
most based on machine learning techniques, for nearly a decade. The data set served well …

The problem of imbalanced data classification learning has received much attention.
Conventional classification algorithms are susceptible to data skew to favor majority …