Most all datasets that hold real-time data have an imbalanced organization of class
instances. The total quantity of instances in certain classes is substantially greater than other …

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

Class imbalance is an active research area in the machine learning community. However,
existing and recent literature showed that class overlap had a higher negative impact on the …

© International Association of Engineers. Mining imbalanced data, which is also known as
a class imbalanced problem is one of the most enormously challenging tasks in machine …

The imbalanced data problems in data mining are common nowadays, which occur due to
skewed nature of data. These problems impact the classification process negatively in …

Classification is a supervised machine learning technique to categorize data into a
predefined and distinct number of classes. Again, in the real world, most of these data set …

In Data mining and Knowledge Discovery hidden and valuable knowledge from the data
sources is discovered. The traditional algorithms used for knowledge discovery are bottle …

Most existing classification approaches assume the underlying training set is evenly
distributed. In class imbalanced classification, the training set for one class (majority) far …

The Imbalanced class problem is a recent challenge in data mining. A dataset is said to be
imbalance when their classification categories are not properly mentioned, and the class …

Classification is one of the most fundamental and well-known tasks in data mining. Class
imbalance is the most challenging issue encountered when performing classification, ie …