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 …

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

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 …

River is a machine learning library for dynamic data streams and continual learning. It
provides multiple state-of-the-art learning methods, data generators/transformers …

Spam emails have been traditionally seen as just annoying and unsolicited emails
containing advertisements, but they increasingly include scams, malware or phishing. In …

In many applications of information systems learning algorithms have to act in dynamic
environments where data are collected in the form of transient data streams. Compared to …

Random forests is currently one of the most used machine learning algorithms in the non-
streaming (batch) setting. This preference is attributable to its high learning performance and …

Concept drift poses a critical challenge to deploy machine learning models to solve practical
security problems. Due to the dynamic behavior changes of attackers (and/or the benign …

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 …