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 …

Despite more than two decades of continuous development learning from imbalanced data
is still a focus of intense research. Starting as a problem of skewed distributions of binary …

Abstract Background and Objective Cerebral stroke has become a significant global public
health issue in recent years. The ideal solution to this concern is to prevent in advance by …

Class imbalance (CI) is a well-known problem in data science. Nowadays, it is affecting the
data modeling of many of the real-world processes that are being digitized. The …

Imbalanced data problem is widely present in network intrusion detection, spam filtering,
biomedical engineering, finance, science, being a challenge in many real-life data-intensive …

Featured Application This study attempts to mitigate the effects of highly imbalanced data in
realizing an enhanced cost-sensitive prediction model. The model intends to enable …

Sensors and Internet of things (IoT) have been widely used in the digitalized orchards.
Traditional disease-pest recognition and early warning systems, which are based on …

Data scarcity and class imbalance are pervasive challenges in machine fault diagnosis,
impeding the development and broad adaptation of accurate and reliable deep-learning …

Abstract “Concept drift” and class imbalance are two challenges for supervised
classifiers.“Concept drift”(or non-stationarity) is changes in the underlying function being …

Learning from data streams is one of the most promising and challenging domains in
modern machine learning. Proliferating online data sources provide us access to real-time …