Class imbalance is a common problem with datasets relating to various areas of life. It
causes many traditional machine learning algorithms to tend to misclassify minority samples …

In this paper we focus on class imbalance issue which often leads to sub-optimal
performance of classifiers. Despite many attempts to solve this problem, there is still a need …

Class imbalance is a common problem in machine learning tasks, which often leads to sub-
optimal performance of classifiers, where the classification of a new example is based on …

Highly imbalanced data, which occurs in many real-world applications, often makes
machine-based processing difficult or even impossible. The over-and under-sampling …

In the field of machine learning, the issue of class imbalance is a common problem. It refers
to an imbalance in the quantity of data collected, where one class has a significantly larger …

The class imbalance problem is prevalent in many classification tasks such as disease
identification using microarray data, network intrusion detection, and so on. These are tasks …

The Class imbalance problem occurs when there are many more instances of some class
than others. ie skewed class distribution. In cases like this, standard classifier tends to be …

The class imbalance problem occurs when one class far outnumbers the other classes,
causing most traditional classifiers perform poorly on the minority classes. To tackle this …

The subject of a class imbalance is a well-investigated topic which addresses performance
degradation of standard learning models due to uneven distribution of classes in a …

Class-imbalanced classification is one of the most challenging issues in supervised
learning. Traditional machine learning classifiers are generally biased toward to the majority …