A Systematic Study of Online Class Imbalance Learning with Concept Drift

Wang, Shuo (2018) A Systematic Study of Online Class Imbalance Learning with Concept Drift. IEEE Transactions on Neural Networks and Learning Systems, 29 (10). pp. 4802-4821. ISSN 2162-237X

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Abstract

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.

Item Type: Article
Additional Information: “© 2018 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”
Uncontrolled Keywords: online class imbalance learning, skewed class distributions, class-imbalanced data streams, concept drift
Subjects: G400 Computer Science
Divisions: Faculty of Computing, Engineering and the Built Environment
Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology > Cyber Security
Depositing User: Shuo Wang
Date Deposited: 11 Jul 2019 14:24
Last Modified: 11 Jul 2019 14:24
URI: http://www.open-access.bcu.ac.uk/id/eprint/7721

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