The dynamicity of real-world systems poses a significant challenge to deployed predictive
machine learning (ML) models. Changes in the system on which the ML model has been …

In recent years, the availability of time series streaming information has been growing
enormously. Learning from real-time data has been receiving increasingly more attention …

A hands-on approach to tasks and techniques in data stream mining and real-time analytics,
with examples in MOA, a popular freely available open-source software framework. Today …

Streaming data are increasingly present in real-world applications such as sensor
measurements, satellite data feed, stock market, and financial data. The main characteristics …

Abstract Concept drift detectors are online learning software that mostly attempt to estimate
the drift positions in data streams in order to modify the base classifier after these changes …

Online learning involves extracting information from large quantities of data (streams)
usually affected by changes in the distribution (concept drift). A drift detector is a small …

Abstract Intrusion Detection Systems (IDS) have become pivotal in safeguarding information
systems against evolving threats. Concurrently, Concept Drift presents a significant …

Online learning regards extracting information from large quantities of data (streams) usually
affected by changes in the distribution (concept drift). Drift detectors are software that …

Abstract Concept drift detectors are software that usually attempt to estimate the positions of
concept drifts in large data streams in order to replace the base learner after changes in the …

Online learning is about extracting information from large data streams which may be
affected by changes in the distribution of the data, events known as concept drift. Concept …