Please use this identifier to cite or link to this item: http://repositorio.inesctec.pt/handle/123456789/5370
Title: A Survey on Concept Drift Adaptation
Authors: João Gama
Zliobaite,I
Bifet,A
Pechenizkiy,M
Bouchachia,A
Issue Date: 2014
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.
URI: http://repositorio.inesctec.pt/handle/123456789/5370
http://dx.doi.org/10.1145/2523813
metadata.dc.type: article
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