Abstract Concept drift may lead to a sharp downturn in the performance of streaming in data-
based algorithms, caused by unforeseeable changes in the underlying distribution of data …

Abstract Concept drift refers to the inevitable phenomenon that influences the statistical
features of the data stream. Detecting concept drift in data streams quickly and precisely …

In data stream mining, one of the biggest challenges is to develop algorithms that deal with
the changing data. As data evolve over time, static models become outdated. This …

Data classification in streams where the underlying distribution changes over time is known
to be difficult. This problem—known as concept drift detection—involves two aspects:(i) …

Data stream mining is a fast growing research topic due to the ubiquity of data in several real-
world problems. Given their ephemeral nature, data stream sources are expected to …

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 …

Concept drift is a phenomenon in which the distribution of a data stream changes over time
in unforeseen ways, causing prediction models built on historical data to become inaccurate …

Abstract Concept drift is one of the key challenges that incremental learning needs to deal
with. So far, a lot of algorithms have been proposed to cope with it, but it is still difficult to …

To decide if an update to a data stream classifier is necessary, existing sliding window
based techniques monitor classifier performance on recent instances. If there is a significant …

Abstract Concept drift detection is a crucial task in data stream evolving environments. Most
of the state of the art approaches designed to tackle this problem monitor the loss of …