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.

Abstract

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)
URI:	http://researchrepository.murdoch.edu.au/id/eprint/59059

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