Many real-world machine learning applications require building models using highly
imbalanced datasets. Usually, in medical datasets, the healthy patients or samples are …

Class imbalance is one of the challenging problems for machine learning algorithms. When
learning from highly imbalanced data, most classifiers are overwhelmed by the majority …

For the imbalanced classification problems, most traditional classification models only focus
on searching for an excellent classifier to maximize classification accuracy with the fixed …

Class imbalance is one of the challenging problems for machine learning in many real-world
applications. Cost-sensitive learning has attracted significant attention in recent years to …

In this paper, we present a new rule induction algorithm for machine learning in medical
diagnosis. Medical datasets, as many other real-world datasets, exhibit an imbalanced class …

The classifier built from a data set with a highly skewed class distribution generally predicts
the more frequently occurring classes much more often than the infrequently occurring …

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 …

In biomedical data, the imbalanced data problem occurs frequently and causes poor
prediction performance for minority classes. It is because the trained classifiers are mostly …

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 …

Real-life datasets are often imbalanced, that is, there are significantly more training samples
available for some classes than for others, and consequently the conventional aim of …