Nowadays learning from imbalanced data sets are a relatively a very critical task for many
data mining applications such as fraud detection, anomaly detection, medical diagnosis …

Imbalanced data learning is a research area and day by day development is going on. Due
to these researchers are motivated to pay attention to find efficient and adaptive methods for …

© 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 learning issue is related to the performance of learning algorithms in the
presence of asymmetrical class distribution. Due to the complex characteristics of …

The application area of technology is expanding the span of information size is also
additionally increases. Classification gets to be troublesome in view of unbounded size and …

Imbalanced data situation is that there are unequal distributions of data samples between
different classes. It usually poses a challenge to any classification methods as it becomes …

Classifiers are trained with datasets of imbalanced class distributions, imbalance big data is
an important problem in data mining. Imbalance in the data occurs when the number of …

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 …

Availability of data from many different applications such as surveillance systems, security
appliances, finances has been continuously expanding. Many machine learning (ML) and …

In Imbalanced datasets, minority classes can be erroneously classified by common
classification algorithms. In this paper, an ensemble-base algorithm is proposed by creating …