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 …

Class overlap in imbalanced datasets is the most common challenging situation for
researchers in the fields of deep learning (DL) machine learning (ML), and big data (BD) …

Malicious URL, aka malicious website, is a common and serious threat to cybersecurity.
Malicious URLs host unsolicited content (spam, phishing, drive-by exploits, etc.) and lure …

The classification in class imbalanced data has drawn significant interest in medical
application. Most existing methods are prone to categorize the samples into the majority …

For the imbalanced classification problems, most traditional classification models only focus
on searching for an excellent classifier to maximize classification accuracy with the fixed …

In this paper, a data-driven fault diagnosis model dealing with chemical imbalanced data
streams is investigated. Different faults occur with varied frequencies by continuous arrival in …

Credit card fraud represents one of the biggest threats for organizations due to the
probability of huge losses associated with them. This paper presents a cost-sensitive Risk …

Imbalanced classification is a challenging task in the fields of machine learning, data mining
and pattern recognition. Cost-sensitive online algorithms are very important methods for …

As an important part of machine learning, classification learning has been applied in many
practical fields. It is valuable that to discuss class imbalance learning in several fields. In this …

Fault diagnosis of mine ventilation system is of great significance for mine safety production.
Traditional machine learning algorithms have been widely applied in the field of mine …