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A survey on concept drift adaptation

Authors:

Indrė Žliobaitė,

Mykola Pechenizkiy,

Abdelhamid BouchachiaAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 46, Issue 4

Article No.: 44, Pages 1 - 37

https://doi.org/10.1145/2523813

Published: 01 March 2014 Publication History

Abstract

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 knowledge of supervised learning in this article, we characterize adaptive learning processes; categorize existing strategies for handling concept drift; overview the most representative, distinct, and popular techniques and algorithms; discuss evaluation methodology of adaptive algorithms; and present a set of illustrative applications. The survey covers the different facets of concept drift in an integrated way to reflect on the existing scattered state of the art. Thus, it aims at providing a comprehensive introduction to the concept drift adaptation for researchers, industry analysts, and practitioners.

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Index Terms

A survey on concept drift adaptation
1. Computing methodologies
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cover image ACM Computing Surveys

ACM Computing Surveys Volume 46, Issue 4

April 2014

463 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/2597757

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Published: 01 March 2014

Accepted: 01 October 2013

Revised: 01 August 2013

Received: 01 February 2012

Published in CSUR Volume 46, Issue 4

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