A Survey on Concept Drift Adaptation

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

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

Journal: ACM Computing Surveys

This data was imported from Scopus:

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

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

Journal: ACM Computing Surveys

Volume: 46

Issue: 4

eISSN: 1557-7341

ISSN: 0360-0300

DOI: 10.1145/2523813

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.

This source preferred by Hamid Bouchachia

This data was imported from Web of Science (Lite):

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

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

Journal: ACM COMPUTING SURVEYS

Volume: 46

Issue: 4

eISSN: 1557-7341

ISSN: 0360-0300

DOI: 10.1145/2523813

The data on this page was last updated at 04:42 on November 17, 2017.