Document Type

Article

Original Publication Date

2017

Journal/Book/Conference Title

Information Fusion

Volume

37

First Page

132

Last Page

156

DOI of Original Publication

10.1016/j.inffus.2017.02.004

Comments

Originally published at https://doi.org/10.1016/j.inffus.2017.02.004

Date of Submission

June 2017

Abstract

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.

Is Part Of

VCU Computer Science Publications

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