A survey on concept drift adaptation. Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., & Bouchachia, A. ACM Computing Surveys, 46(4):44:1–44:37, March, 2014. ![A survey on concept drift adaptation [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@article{gama_survey_2014,
title = {A survey on concept drift adaptation},
volume = {46},
issn = {0360-0300},
url = {https://doi.org/10.1145/2523813},
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.},
number = {4},
urldate = {2020-03-19},
journal = {ACM Computing Surveys},
author = {Gama, João and Žliobaitė, Indrė and Bifet, Albert and Pechenizkiy, Mykola and Bouchachia, Abdelhamid},
month = mar,
year = {2014},
keywords = {Concept drift, adaptive learning, change detection, data streams},
pages = {44:1--44:37},
}
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