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

[img]
Preview
Text
A Systematic Study of Online Class Imbalance Learning with Concept Drift.pdf
Available under License Creative Commons Attribution.

Download (980kB)

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.”
Identification Number: https://doi.org/10.1109/TNNLS.2017.2771290
Dates:
DateEvent
2 November 2017Accepted
4 January 2018Published
Uncontrolled Keywords: online class imbalance learning, skewed class distributions, class-imbalanced data streams, concept drift
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - 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
Depositing User: Shuo Wang
Date Deposited: 11 Jul 2019 14:24
Last Modified: 22 Mar 2023 12:01
URI: https://www.open-access.bcu.ac.uk/id/eprint/7721

Actions (login required)

View Item View Item

Research

In this section...