Murdoch University Research Repository

Welcome to the Murdoch University Research Repository

The Murdoch University Research Repository is an open access digital collection of research
created by Murdoch University staff, researchers and postgraduate students.

Learn more

Classification of multi-class imbalanced data streams using a dynamic data-balancing technique

Mohammed, R.A., Wong, K.W., Shiratuddin, M.F. and Wang, X.ORCID: 0000-0002-1557-8265 (2020) Classification of multi-class imbalanced data streams using a dynamic data-balancing technique. In: Yang, H., Pasupa, K., Chi-Sing Leung, A., Kwok, J.T., Chan, J.H. and King, I., (eds.) Neural Information Processing. Springer, Cham, pp. 279-290.

Link to Published Version:
*Subscription may be required


The performance of classification algorithms with imbalanced streaming data depends upon efficient re-balancing strategy for learning tasks. The difficulty becomes more elevated with multi-class highly imbalanced streaming data. In this paper, we investigate the multi-class imbalance problem in data streams and develop an adaptive framework to cope with imbalanced data scenarios. The proposed One-Vs-All Adaptive Window re-Balancing with Retain Knowledge (OVA-AWBReK) classification framework will combine OVA binarization with Automated Re-balancing Strategy (ARS) using Racing Algorithm (RA). We conducted experiments on highly imbalanced datasets to demonstrate the use of the proposed OVA-AWBReK framework. The results show that OVA-AWBReK framework can enhance the classification performance of the multi-class highly imbalanced data.

Item Type: Book Chapter
Murdoch Affiliation(s): Information Technology, Mathematics and Statistics
Publisher: Springer, Cham
Copyright: © 2020 Springer Nature Switzerland AG
Other Information: Part of the Communications in Computer and Information Science book series (CCIS, volume 1333)
Item Control Page Item Control Page