A survey on concept drift adaptation

J. Gama, I. Zliobaite, A. Bifet, M. Pechenizkiy, A. Bouchachia

Research output: Contribution to journalArticleAcademicpeer-review

946 Citations (Scopus)
1 Downloads (Pure)

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.
Original languageEnglish
Article number44
Pages (from-to)44/1-37
Number of pages37
JournalACM Computing Surveys
Volume46
Issue number4
DOIs
Publication statusPublished - 2014

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