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