The class imbalance problem has become a leading challenge. Although conventional
imbalance learning methods are proposed to tackle this problem, they have some …

The class imbalance problem has posed a leading challenge in real-world applications.
Traditional methods focus on either the data level or algorithm level to solve the binary …

Student imbalanced data is one of the problems in data mining community. To state the
student dropout problem, an ensemble method with under-sampling technique is applied for …

Classification of data with imbalanced characteristics is an essential research problem as
the data from most real-world applications follow non-uniform class proportions. Solutions to …

The class imbalance problem is important and challenging. Ensemble approaches are
widely used to tackle this problem because of their effectiveness. However, existing …

Class imbalance problem is being manifoldly confronted by researchers due to the
increasing amount of complicated data. Common classification algorithms are impoverished …

Current generation real-world data sets processed through machine learning are
imbalanced by nature. This imbalanced data enables the researchers with a challenging …

Learning with class imbalanced data sets is a challenging undertaking by the common
learning algorithms. These algorithms favor majority class due to imbalanced class …