A Survey on Concept Drift Adaptation

• Type: Article in International Scientific Journal
• Date: 2014

Authors

João Gama (Author) FEP; Indre Zliobaite (Author) Other; Albert Bifet (Author) Other

Journal

Title: ACM Computing Surveys

Vol. 46 No. 1

Pages: 44:1-44:37

ISSN: 0360-0300

Publisher: ACM

Indexing

ISI Web of Knowledge - 1972 Citations

ISI Web of Science

Scopus - 2407 Citations

Scientific classification

FOS: Natural sciences

Other information

Authenticus ID: P-009-DVX

DOI: 10.1145/2523813

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 37