Ensemble-based methods are among the most widely used techniques for data stream
classification. Their popularity is attributable to their good performance in comparison to …

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

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 …

Recent advances in computational intelligent systems have focused on addressing complex
problems related to the dynamicity of the environments. In increasing number of real world …

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 …

Data stream prediction is challenging when concepts drift, processing time, and memory
constraints come into account. Concept drift refers to changes in data distribution over time …

Mining and analysing streaming data is crucial for many applications, and this area of
research has gained extensive attention over the past decade. However, there are several …

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

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 …