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 continuous expansion of data availability in many large-scale, complex, and
networked systems, such as surveillance, security, Internet, and finance, it becomes critical …

Binary classifiers are routinely evaluated with performance measures such as sensitivity and
specificity, and performance is frequently illustrated with Receiver Operating Characteristics …

Historical data sets for fault stage diagnosis in industrial machines are often imbalanced and
consist of multiple categories or classes. Learning discriminative models from such data sets …

Extreme learning machine (ELM) is a competitive machine learning technique, which is
simple in theory and fast in implementation. The network types are “generalized” single …

Automated detection of blood vessel structures is becoming of crucial interest for better
management of vascular disease. In this paper, we propose a new infinite active contour …

This paper presents a novel adaptive synthetic (ADASYN) sampling approach for learning
from imbalanced data sets. The essential idea of ADASYN is to use a weighted distribution …

Traditional classification algorithms can be limited in their performance on highly
unbalanced data sets. A popular stream of work for countering the problem of class …

Traditional learning algorithms applied to complex and highly imbalanced training sets may
not give satisfactory results when distinguishing between examples of the classes. The …

Classification of microcalcification clusters from mammograms plays essential roles in
computer-aided diagnosis for early detection of breast cancer, where support vector …