Online Ensemble of Ensemble OVA Framework for Class Evolution with Dominant Emerging Classes

Zhi Cao1; Shuyi Zhang1; Chin-Teng Lin2

10.1109/ICDM58522.2023.00109

2023

23rd IEEE International Conference on Data Mining (IEEE ICDM)

1550-4786

979-8-3503-0789-4

2023 IEEE International Conference on Data Mining (ICDM)

968-973

1-4 Dec. 2023

Shanghai, China

10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA

IEEE COMPUTER SOC

In real-world data stream mining, the composition of classes undergoes unpredictable changes, giving rise to the challenge of class evolution, encompassing class emergence, disappearance, and reoccurrence. However, most existing approaches require the storage of past data to adapt their model. While some studies have focused on online learning approaches, they are built on an underlying assumption that the number of instances in any single class is consistently less than the sum of other classes. This assumption becomes invalid when a class emerges with a dominant amount, e.g., news about a pandemic outbreak, harming the performance of existing methods. In this paper, we thoroughly investigate this scenario and propose a novel online ensemble of ensemble one-versus-all framework (EWE) to handle class evolution adaptively. The novel ensemble of ensemble architecture boosts diversity in each one-versus-all classifier. A novel adaptive model adaptation method is also designed to balance the error feedback between the emerging class and the other classes. A confidence-triggered fallback mode is integrated to prevent performance drop due to a wrong decision regarding class disappearance. Experimental studies are conducted on both synthetic and real-world data streams to show that our method achieves higher accuracy in diverse class evolution scenarios compared with the state-of-the-art method, particularly when classes emerge with dominant amounts.

data stream mining online learning class evolution ensemble model

第一

英语

CPCI-S

Australian Research Council (ARC)["DP210101093","DP220100803"]

Computer Science

Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Computer Science, Theory & Methods

WOS:001165180100101

IEEE

1.Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
2.CIBCI Lab, School of Computer Science, University of Technology, Sydney, Australia

Zhi Cao,Shuyi Zhang,Chin-Teng Lin. Online Ensemble of Ensemble OVA Framework for Class Evolution with Dominant Emerging Classes[C]. 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA:IEEE COMPUTER SOC,2023:968-973.