Concept drift describes unforeseeable changes in the underlying distribution of streaming
data overtime. Concept drift research involves the development of methodologies and …

Incremental learning, online learning, and data stream learning are terms commonly
associated with learning algorithms that update their models given a continuous influx of …

The problem of concept drift has gained a lot of attention in recent years. This aspect is key
in many domains exhibiting non-stationary as well as cyclic patterns and structural breaks …

Concept drift techniques aim at learning patterns from data streams that may change over
time. Although such behavior is not usually expected in controlled environments, real-world …

Data stream research has grown rapidly over the last decade. Two major features
distinguish data stream from batch learning: stream data are generated on the fly, possibly in …

As advanced metering infrastructure (AMI) is responsible for collecting, measuring, and
analyzing energy usage data, as well as transmitting this information from a smart meter to a …

Mining non-stationary stream is a challenging task due to its unique property of infinite
length and dynamic characteristics let alone the issues of concept drift, concept evolution …

In todays digital era, a massive amount of streaming data is automatically and continuously
generated. To learn such data streams, many algorithms have been proposed during the …

Nowadays, many real-world applications of our daily life generate massive volume of
streaming data at a higher speed than ever before, to name a few, Web clicking data …

In a dynamic stream there is an assumption that the underlying process generating the
stream is non-stationary and that concepts within the stream will drift and change as the …