BEDCOE: Borderline Enhanced Disjunct Cluster Based Oversampling Ensemble for Online Multi-Class Imbalance Learning

Shuxian Li, Liyan Song, Yiu Ming Cheung*, Xin Yao*

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review


Multi-class imbalance learning usually confronts more challenges especially when learning from streaming data. Most existing methods focus on manipulating class imbalance ratios, disregarding other data properties such as the borderline and the disjunct. Recent studies have shown non-negligible impact of disregarding these properties on deteriorating predictive performance. Online multi-class imbalance would further exacerbate such negative impact. To abridge the research gap of online multi-class imbalance learning, we propose to enhance the number of training times of borderline samples based on the disjunct class-wise clusters that are adaptively constructed over time for each class individually. Specifically, we propose a borderline enhanced strategy for ensemble aiming to increase the number of training times of samples neighboring to borderline areas of different classes. We also propose to generate synthetic samples for training based on the adaptively learned disjunct clusters that are maintained for each class individually online, catering for online multi-class imbalance problem directly. These two components construct the Borderline Enhanced Disjunct Cluster Based Oversampling Ensemble (BEDCOE). Experimental studies are conducted and demonstrate the effectiveness of BEDCOE and each of its components in dealing with online multi-class imbalance.

Original languageEnglish
Title of host publicationECAI 2023 - 26th European Conference on Artificial Intelligence, including 12th Conference on Prestigious Applications of Intelligent Systems, PAIS 2023 - Proceedings
EditorsKobi Gal, Ann Nowé, Grzegorz J. Nalepa, Roy Fairstein, Roxana Rădulescu
PublisherIOS Press BV
Number of pages8
ISBN (Electronic)9781643684376
ISBN (Print)9781643684369
Publication statusPublished - 30 Sept 2023
Event26th European Conference on Artificial Intelligence, ECAI 2023 - Krakow, Poland
Duration: 30 Sept 20234 Oct 2023

Publication series

NameFrontiers in Artificial Intelligence and Applications
ISSN (Print)0922-6389
ISSN (Electronic)1879-8314


Conference26th European Conference on Artificial Intelligence, ECAI 2023
Internet address

Scopus Subject Areas

  • Artificial Intelligence

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