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Ensemble learning for data stream analysis: a survey

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journal contribution
posted on 02.02.2017, 15:35 by Bartosz Krawczyk, Leandro L. Minku, Joao Gama, Jerzy Stefanowski, Michal Wozniak
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 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 semi-supervised learning, complex data representations and structured outputs. The paper concludes with a discussion of open research problems and lines of future research.

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Citation

Information Fusion, 2017, 37, pp. 132–156

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Computer Science

Version

AM (Accepted Manuscript)

Published in

Information Fusion

Publisher

Elsevier

issn

1566-2535

Acceptance date

01/02/2017

Copyright date

2017

Available date

03/08/2018

Publisher version

http://www.sciencedirect.com/science/article/pii/S1566253516302329

Notes

The file associated with this record is embargoed until 18 months after the date of publication. The final published version may be available through the links above. Following the embargo period the above license applies.

Language

en

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