Medical datasets are usually imbalanced, where negative cases severely outnumber
positive cases. Therefore, it is essential to deal with this data skew problem when training …

Most classification algorithms assume that classes are in a balanced state. However,
datasets with class imbalances are everywhere. The classes of actual medical datasets are …

In recent decades, machine learning algorithms have been used in different fields; one of
the most used fields is the health sector. Biomedical data are usually extensive in size, and …

With the development of artificial intelligence, big data classification technology provides the
advantageous help for the medicine auxiliary diagnosis research. While due to the different …

Real-world datasets in many domains like medical, intrusion detection, fraud transactions
and bioinformatics are highly imbalanced. In classification problems, imbalanced datasets …

Imbalanced Data (ID) is a problem that deters Machine Learning (ML) models from
achieving satisfactory results. ID is the occurrence of a situation where the quantity of the …

Imbalanced data classification is challenging task for various datasets in the real world. One
of technique to enlarge the sample in minority class is oversampling to fix size as majority …

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 …

Classification of imbalanced data remains an important field in machine learning. Several
methods have been proposed to address the class imbalance problem including data …

The classification in class imbalanced data has drawn significant interest in medical
application. Most existing methods are prone to categorize the samples into the majority …