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 …

A dataset is imbalanced if the classification categories are not approximately equally
represented. Recent years brought increased interest in applying machine learning …

Defect models that are trained on class imbalanced datasets (ie, the proportion of defective
and clean modules is not equally represented) are highly susceptible to produce inaccurate …

Automatic fault detection and diagnosis (AFDD) for chillers has significant impacts on energy
saving, indoor environment comfort and systematic building management. Recent works …

The problem of modeling binary responses by using cross-sectional data has been
addressed with a number of satisfying solutions that draw on both parametric and …

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 …

There are several aspects that might influence the performance achieved by existing
learning systems. It has been reported that one of these aspects is related to class …

The 30 m resolution US Department of Agriculture (USDA) crop data layer (CDL) is a widely
used crop type map for agricultural management and assessment, environmental impact …

The class imbalance problem is one of the (relatively) new problems that emerged when
machine learning matured from an embryonic science to an applied technology, amply used …

Rare objects are often of great interest and great value. Until recently, however, rarity has
not received much attention in the context of data mining. Now, as increasingly complex real …