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 …

In a majority–minority classification problem, class imbalance in the dataset (s) can
dramatically skew the performance of classifiers, introducing a prediction bias for the …

In the printed circuit board (PCB) industry, cosmetic defect detection is an essential process
to ensure product quality. However, existing PCB cosmetic defect detection approaches …

Background The large and growing number of published studies, and their increasing rate of
publication, makes the task of identifying relevant studies in an unbiased way for inclusion in …

Most existing classification approaches assume the underlying training set is evenly
distributed. In class imbalanced classification, the training set for one class (majority) far …

Learning unbiased models on imbalanced datasets is a significant challenge. Rare classes
tend to get a concentrated representation in the classification space which hampers the …

In an actual industrial scenario, machines typically operate normally for the majority of the
time, with malfunctions occurring only occasionally. As a result, there is very little recorded …

Climate-related extended outbreaks and range shifts of destructive bark beetle species pose
a serious threat to urban boreal forests in North America and Fennoscandia. Recent …

Many multimedia systems stream real-time visual data continuously for a wide variety of
applications. These systems can produce vast amounts of data, but few studies take …

Many real world datasets don't offer enough training input for regular classifiers: some
classes are more represented than others. Imbalanced data raises problems in Machine …