Learning under Concept Drift: A Review

• Type: Article in International Scientific Journal
• Date: 2019

Authors

Lu, J (Author) Other; Liu, AJ (Author) Other; Dong, F (Author) Other; Gu, F (Author) Other; João Gama (Author) FEP; Zhang, GQ (Author) Other

Journal

Title: IEEE Transactions on Knowledge and Data Engineering

Vol. 31

Pages: 2346-2363

ISSN: 1041-4347

Publisher: IEEE

Indexing

ISI Web of Knowledge - 555 Citations

Scopus - 707 Citations

Other information

Authenticus ID: P-00Q-VPQ

DOI: 10.1109/tkde.2018.2876857

Abstract (EN): Concept drift describes unforeseeable changes in the underlying distribution of streaming data over time. Concept drift research involves the development of methodologies and techniques for drift detection, understanding, and adaptation. Data analysis has revealed that machine learning in a concept drift environment will result in poor learning results if the drift is not addressed. To help researchers identify which research topics are significant and how to apply related techniques in data analysis tasks, it is necessary that a high quality, instructive review of current research developments and trends in the concept drift field is conducted. In addition, due to the rapid development of concept drift in recent years, the methodologies of learning under concept drift have become noticeably systematic, unveiling a framework which has not been mentioned in literature. This paper reviews over 130 high quality publications in concept drift related research areas, analyzes up-to-date developments in methodologies and techniques, and establishes a framework of learning under concept drift including three main components: concept drift detection, concept drift understanding, and concept drift adaptation. This paper lists and discusses 10 popular synthetic datasets and 14 publicly available benchmark datasets used for evaluating the performance of learning algorithms aiming at handling concept drift. Also, concept drift related research directions are covered and discussed. By providing state-of-the-art knowledge, this survey will directly support researchers in their understanding of research developments in the field of learning under concept drift.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 18