Algorithm Recommendation for Data Streams

Pavel Brazdil; Jan N. van Rijn; Carlos Soares; Joaquin Vanschoren

doi:10.1007/978-3-030-67024-5_11

Algorithm Recommendation for Data Streams

Pavel Brazdil (Corresponding author)
, Jan N. van Rijn
, Carlos Soares
, Joaquin Vanschoren

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

1 Citation (Scopus)

2 Downloads (Pure)

Abstract

This chapter focuses on metalearning approaches that have been applied to data streams. This is an important area, as many real-world data arrive in the form of a stream of observations. We first review some important aspects of the data stream setting, which may involve online learning, non-stationarity, and concept drift.

Original language	English
Title of host publication	Metalearning
Publisher	Springer
Chapter	11
Pages	201-218
Number of pages	18
ISBN (Electronic)	978-3-030-67024-5
ISBN (Print)	978-3-030-67023-8
DOIs	https://doi.org/10.1007/978-3-030-67024-5_11
Publication status	Published - 22 Feb 2022

Publication series

Name	Cognitive Technologies
ISSN (Print)	1611-2482
ISSN (Electronic)	2197-6635

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Publisher Copyright:
© 2022, The Author(s).

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10.1007/978-3-030-67024-5_11

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Algorithm Recommendation for Data Streams

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