Ensemble learning for data stream analysis: A survey

Bartosz Krawczyk, Leandro L. Minku, João Gama, Jerzy Stefanowski, Michał Woźniak
2017 Information Fusion  
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 static data mining, processing streams imposes new computational requirements for algorithms to incrementally process incoming examples while using limited memory and time. Furthermore, due to the non-stationary characteristics of streaming data, prediction models are often also required to adapt to concept drifts. Out of
more » ... several new proposed stream algorithms, ensembles play an important role, in particular for non-stationary environments. This paper surveys research on ensembles for data stream classification as well as regression tasks. Besides presenting a comprehensive spectrum of ensemble approaches for data streams, we also discuss advanced learning concepts such as imbalanced data streams, novelty detection, active and semisupervised learning, complex data representations and structured outputs. The paper concludes with a discussion of open research problems and lines of future research. Published by Elsevier B.V.
doi:10.1016/j.inffus.2017.02.004 fatcat:rfc735znxjcwdebcbjxbyx7xki