A Systematic Study of Online Class Imbalance Learning with Concept Drift

Shuo WANG, Leandro L. MINKU, Xin YAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

208 Citations (Scopus)

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. © 2012 IEEE.
Original languageEnglish
Article number8246564
Pages (from-to)4802-4821
Number of pages20
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume29
Issue number10
Early online date4 Jan 2018
DOIs
Publication statusPublished - Oct 2018
Externally publishedYes

Bibliographical note

This work was supported in part by the Ministry of Science and Technology of China under Grant 2017YFC0804003, in part by the Science and Technology Innovation Committee Foundation of Shenzhen under Grant ZDSYS201703031748284, in part by EPSRC under Grant EP/K001523/1 and Grant EP/J017515/1, and in part by the National Natural Science Foundation of China under Grant 61329302.

Keywords

  • Class imbalance
  • concept drift
  • online learning
  • resampling

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