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 …

Abstract Concept drifts occurring in data streams will jeopardize the accuracy and stability of
the online learning process. If the data stream is imbalanced, it will be even more …

In many real-world classification tasks, data arrives over time and the target concept to be
learned from the data stream may change over time. Boosting methods are well-suited for …

Classifying a stream of non-stationary data with recurrent drift is a challenging task and has
been considered as an interesting problem in recent years. All of the existing approaches …

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 …

The dynamic and unstable nature observed in real world applications influences learning
systems through changes in data, context and resource availability. Data stream mining …

In Recent years data stream classification has been an extensively studied research
problem. Data streams are continuous and rapid flow of data. Data streams include Call …

In data stream mining, predictive models typically suffer drops in predictive performance due
to concept drift. As enough data representing the new concept must be collected for the new …

It is common in real‐world data streams that previously seen concepts will reappear, which
suggests a unique kind of concept drift, known as recurring concepts. Unfortunately, most of …