Nowadays, healthcare is the prime need of every human being in the world, and clinical
datasets play an important role in developing an intelligent healthcare system for monitoring …

A highly-skewed class distribution usually causes the learned classifier to predict the
majority class much more often than the minority class. This is a consequence of the fact that …

The problem of imbalanced data classification often exists in medical diagnosis. Traditional
classification algorithms usually assume that the number of samples in each class is similar …

Class imbalance issue that presents in many real-world datasets exhibit favouritism toward
the majority class and showcases poor performance for the minority class. Such …

Classification of data with imbalanced class distribution has posed a significant drawback of
the performance attainable by most standard classifier learning algorithms, which assume a …

Class imbalance problem occurs when the number of training instances belonging to
different classes are clearly different. In this scenario, many traditional classifiers often fail to …

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

In recent years, most researchers focused on the classification problems of imbalanced data
sets, and these problems are widely distributed in industrial production and medical …

A number of classification problems need to deal with data imbalance between classes.
Often it is desired to have a high recall on the minority class while maintaining a high …

Class imbalance learning is a recent topic, which helps us to detect the classes from
unbalanced datasets. In various real scenarios, where we need to find the exceptional cases …