Learning from imbalanced data is an important and challenging topic in machine learning.
Many works have devised methods to cope with imbalanced data, but most methods only …

In many real-world problems, the datasets are imbalanced when the samples of majority
classes are much greater than the samples of minority classes. In general, machine learning …

Imbalanced data classification is an important research topic in real-world applications, like
fault diagnosis in an aircraft manufacturing system. The over-sampling method is often used …

Class imbalance is a widely existed and challenging problem in real-world applications
such as disease diagnosis, fraud detection, network intrusion detection and so on. Due to …

Learning from imbalanced datasets is highly demanded in real-world applications and a
challenge for standard classifiers that tend to be biased towards the classes with the majority …

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

A class imbalance problem occurs when a dataset is decomposed into one majority class
and one minority class. This problem is critical in the machine learning domains because it …

Ensemble models achieve high accuracy by combining a number of base estimators and
can increase the reliability of machine learning compared to a single estimator. Additionally …

Learning from imbalanced datasets is a challenging problem in machine learning research
since the performance of the traditional classifiers suffer from biased classification towards …

Discovering hidden pattern from imbalanced data is a critical issue in various real-world
applications. Existing classification methods usually suffer from the limitation of data …