Learning data sampled from a nonstationary distribution has been shown to be a very
challenging problem in machine learning, because the joint probability distribution between …

Learning in nonstationary environments, also known as learning concept drift, is concerned
with learning from data whose statistical characteristics change over time. Concept drift is …

Although the issues of concept drift and class imbalance have been studied separately, the
joint problem is underexplored even though it has received increasing attention. Concept …

As the proliferation of constant data feeds increases from social media, embedded sensors,
and other sources, the capability to provide predictive concept labels to these data streams …

In the real world data often arrives in streams and evolves over time. Concept drift in
supervised learning means that the relation between the input data and the target variable …

With the advancement of information technology, organizations newlinetend to generate a
tremendous amount of high-velocity data streams. The newlinestaggering growth of such …

Online data stream learning requires to process each training example upon arrival.
Machine learning algorithms designed to process data in this manner are particularly …

Applications that generate data in the form of fast streams from non-stationary environments,
that is, those where the underlying phenomena change over time, are becoming …

In the real world, most of the applications are inherently dynamic in nature ie their underlying
data distribution changes with time. As a result, the concept drifts occur very frequently in the …

Due to the digital era, and recent development in software and hardware technology uses
enormous applications like e-commerce, mailing system, social media, fraud detection …