Recently, concept drift has become an important issue while analyzing non-stationary
distribution data in data mining. For example, data streams carry a characteristic that data …

Difficulties of learning from nonstationary data stream are generally twofold. First,
dynamically structured learning framework is required to catch up with the evolution of …

Continual learning from data streams is among the most important topics in contemporary
machine learning. One of the biggest challenges in this domain lies in creating algorithms …

Stream-mining approach is defined as a set of cutting-edge techniques designed to process
streams of data in real time, in order to extract knowledge. In the particular case of …

One of the most important challenges for machine learning community is to develop efficient
classifiers which are able to cope with data streams, especially with the presence of the so …

Abstract Knowledge extraction from data streams has attracted attention in recent years due
to its wide range of applications, including sensor networks, web clickstreams, and user …

2 Method Methods for evaluating drift reaction times are calculated based on moments in the
stream when a classifier starts to recover or fully recovers after a drift. It is worth noticing that …

The treatment of large data streams in the presence of concept drifts is one of the main
challenges in the field of data mining, particularly when the algorithms have to deal with …

Among the recently published works in the field of data stream analysis–both in the context
of classification task and concept drift detection–the deficit of real-world data streams is a …