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. © 2023 The Authors.
|Title of host publication
|ECAI 2023 : 26th European Conference on Artificial Intelligence, September 30–October 4, 2023, Kraków, Poland, Including 12th Conference on Prestigious Applications of Intelligent Systems (PAIS 2023) : Proceedings
|Kobi GAL, Ann NOWÉ, Grzegorz J. NALEPA, Roy FAIRSTEIN, Roxana RĂDULESCU
|IOS Press BV
|Number of pages
|Published - 28 Sept 2023
|Frontiers in Artificial Intelligence and Applications
This work was supported by National Natural Science Foundation of China (NSFC) under Grant No. 62002148 and Grant No. 62250710682, Guangdong Provincial Key Laboratory under Grant No. 2020B121201001, the Program for Guangdong Introducing Innovative and Enterpreneurial Teams under Grant No. 2017ZT07X386, and Research Institute of Trustworthy Autonomous Systems (RITAS).