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A Systematic Study of Online Class Imbalance Learning with Concept Drift

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journal contribution
posted on 14.02.2018, 15:28 by Shuo Wang, Leandro L. Minku, Xin Yao
As an emerging research topic, online class imbalance learning often combines the challenges of both class imbalance and concept drift. It deals with data streams having very skewed class distributions, where concept drift may occur. It has recently received increased research attention; however, very little work addresses the combined problem where both class imbalance and concept drift coexist. As the first systematic study of handling concept drift in class-imbalanced data streams, this paper first provides a comprehensive review of current research progress in this field, including current research focuses and open challenges. Then, an in-depth experimental study is performed, with the goal of understanding how to best overcome concept drift in online learning with class imbalance. Based on the analysis, a general guideline is proposed for the development of an effective algorithm.

Funding

This work was supported by the Ministry of Science and Technology of China (Grant No. 2017YFC0804003), the Science and Technology Innovation Committee Foundation of Shenzhen (Grant No. ZDSYS201703031748284), EPSRC (Grant Nos. EP/K001523/1 and EP/J017515/1) and the National Natural Science Foundation of China (Grant No. 61329302).

History

Citation

IEEE Transactions on Neural Networks and Learning Systems, 2018, PP(99)

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Informatics

Version

AM (Accepted Manuscript)

Published in

IEEE Transactions on Neural Networks and Learning Systems

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

issn

2162-237X

eissn

2162-2388

Acceptance date

01/11/2017

Copyright date

2018

Available date

14/02/2018

Publisher version

http://ieeexplore.ieee.org/document/8246564/

Language

en

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