A survey on concept drift adaptation

Authors: Gama, J., Zliobaite, I., Bifet, A., Pechenizkiy, M. and Bouchachia, A.

Journal: ACM Computing Surveys

Volume: 46

Issue: 4

eISSN: 1557-7341

ISSN: 0360-0300

DOI: 10.1145/2523813

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. © 2014 ACM.

https://eprints.bournemouth.ac.uk/22491/

Source: Scopus

A Survey on Concept Drift Adaptation

Authors: Gama, J., Zliobaite, I., Bifet, A., Pechenizkiy, M. and Bouchachia, A.

Journal: ACM COMPUTING SURVEYS

Volume: 46

Issue: 4

eISSN: 1557-7341

ISSN: 0360-0300

DOI: 10.1145/2523813

https://eprints.bournemouth.ac.uk/22491/

Source: Web of Science (Lite)

Preferred by: Hamid Bouchachia

A Survey on Concept Drift Adaptation

Authors: Gama, J., Zliobaite, I., Bifet, A., Pechenizkiy, M. and Bouchachia, A.

Journal: ACM Computing Surveys

https://eprints.bournemouth.ac.uk/22491/

Source: Manual

A Survey on Concept Drift Adaptation

Authors: Gama, J., Zliobaite, I., Bifet, A., Pechenizkiy, M. and Bouchachia, A.

Journal: ACM Computing Surveys

Volume: 46

Issue: 4

Pages: 44

ISSN: 0360-0300

Abstract:

Concept drift primarily refers to an online supervised learning scenario when the relation between the in- put data and the target variable changes over time. Assuming a general knowledge of supervised learning in this paper we characterize adaptive learning process, categorize existing strategies for handling concept drift, discuss the most representative, distinct and popular techniques and algorithms, discuss evaluation methodology of adaptive algorithms, and present a set of illustrative applications. This introduction to the concept drift adaptation presents the state of the art techniques and a collection of benchmarks for re- searchers, industry analysts and practitioners. The survey aims at covering the different facets of concept drift in an integrated way to reflect on the existing scattered state-of-the-art.

https://eprints.bournemouth.ac.uk/22491/

Source: BURO EPrints