Abstract Concept drift is one of the most prominent issues in streaming data that machine
learning models need to address. Most of the research in the field of concept drift targets …

Last decade demonstrate the massive growth in organizational data which keeps on
increasing multi‐fold as millions of records get updated every second. Handling such vast …

Various types of online learning algorithms have been developed so far to handle concept
drift in data streams. We perform more detailed evaluation of these algorithms through new …

This study proposes a novel incremental learning algorithm called distribution matching
ensemble (DME) in context of adaptive weighted ensemble learning. In particular, DME …

In streaming data environments, data characteristics and probability distributions are likely to
change over time, causing a phenomenon called concept drift, which poses challenges for …

The term concept drift refers to the change of distribution underlying the data. It is an
inherent property of evolving data streams. Concept drift detection and adaptation has been …

Learning in non-stationary environments remains challenging due to dynamic and unknown
probability distribution. This issue is even more problematic when there is a lack of …

In machine learning and predictive analytics, the underlying data distributions tend to
change with the course of time known as concept drift. Accurate labelling in case of …

Adapting to drifting data streams is a significant challenge in online learning. Concept drift
must be detected for effective model adaptation to evolving data properties. Concept drift …

In increasing number of real world applications, data are presented as streams that may
evolve over time and this is known by concept drift. Handling concept drift through ensemble …