A multi-objective ensemble method for online class imbalance learning

Shuo Wang*, Leandro L. Minku, Xin Yao

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)


Online class imbalance learning is an emerging learning area that combines the challenges of both online learning and class imbalance learning. In addition to the learning difficulty from the imbalanced distribution, another major challenge is that the imbalanced rate in a data stream can be dynamically changing. OOB and UOB are two state-of-the-art methods for online class imbalance problems [1]. UOB is better at recognizing minority-class examples when the imbalance rate does not change much over time, while OOB is more prepared for the case with a dynamic rate. Aiming for an effective method for both static and dynamic cases, this paper proposes a multi-objective ensemble method MOSOB that combines OOB and UOB. MOSOB finds the Pareto-optimal weights for OOB and UOB at each time step, to maximize minority-class recall and majority-class recall simultaneously. Experiments on five real-world data applications show that MOSOB performs well in both static and dynamic data streams. Furthermore, we look into its performance on a group of highly imbalanced data streams. To respond to the minority class within 10000 time steps, the imbalance rate can be as low as 0.1% for easy data streams; at least 3% of imbalance rate is required to classify difficult data streams.

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages8
ISBN (Print)9781479914845
Publication statusPublished - 3 Sept 2014
Event2014 International Joint Conference on Neural Networks, IJCNN 2014 - Beijing, China
Duration: 6 Jul 201411 Jul 2014


Conference2014 International Joint Conference on Neural Networks, IJCNN 2014

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence


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