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 …

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

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 …

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 …

The increment of computer technology use and the continued growth of companies have
enabled most financial transactions to be performed through the electronic commerce …

Concept drift primarily refers to an online supervised learning scenario when the relation
between the input data and the target variable changes over time. Assuming a general …

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 …

In this work we present a machine learning based approach for detecting drifting behavior–
so-called concept drifts–in continuous data streams. The motivation for this contribution …

Abstract Concept drift is a phenomenon that commonly happened in data streams and need
to be detected, because it means the statistical properties of a target variable, which the …

Incremental and online learning algorithms are more relevant in the data mining context
because of the increasing necessity to process data streams. In this context, the target …