Current research on imbalanced data recognises that class imbalance is aggravated by
other data intrinsic characteristics, among which class overlap stands out as one of the most …

Credit scoring analysis has gained tremendous importance for researchers and the financial
industries around the globe. It helps the financial industries to grant credits or loans to each …

The class-imbalance problem is an important area that plagues machine learning and data
mining researchers. It is ubiquitous in all areas of the real world. At present, many methods …

The combination of class imbalance and overlap is currently one of the most challenging
issues in machine learning. While seminal work focused on establishing class overlap as a …

Many binary class datasets in real-life applications are affected by class imbalance problem.
Data complexities like noise examples, class overlap and small disjuncts problems are …

Since most classifiers are biased toward the dominant class, class imbalance is a
challenging problem in machine learning. The most popular approaches to solving this …

The problem of imbalanced class distribution in real-world datasets severely impairs the
performance of classification algorithms. The learning task becomes more complicated and …

Twitter has become a unique platform for social interaction from people all around the world,
leading to an extensive amount of knowledge that can be used for various reasons. People …

Accurate diagnosis of breast cancer using automated algorithms continues to be a
challenge in the literature. Although researchers have conducted a great deal of work to …

Imbalanced datasets can adversely affect classifier performance. Conventional
undersampling approaches may lead to the loss of essential information, while …