A Systematic Study of Online Class Imbalance Learning With Concept Drift

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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.

Original languageEnglish
Pages (from-to)4802-4821
Number of pages20
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number10
Early online date4 Jan 2018
Publication statusPublished - Oct 2018


  • concept drift
  • resampling
  • Online learning
  • class imbalance


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