Book chapter
Classification of multi-class imbalanced data streams using a dynamic data-balancing technique
Neural Information Processing, Vol.1333, pp.279-290
Springer, Cham
2020
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.
Details
- Title
- Classification of multi-class imbalanced data streams using a dynamic data-balancing technique
- Authors/Creators
- R.A. Mohammed (Author/Creator)K.W. Wong (Author/Creator)M.F. Shiratuddin (Author/Creator)X. Wang (Author/Creator)
- Contributors
- H. Yang (Editor)K. Pasupa (Editor)A. Chi-Sing Leung (Editor)J.T. Kwok (Editor)J.H. Chan (Editor)I. King (Editor)
- Publication Details
- Neural Information Processing, Vol.1333, pp.279-290
- Publisher
- Springer, Cham
- Identifiers
- 991005543506807891
- Copyright
- © 2020 Springer Nature Switzerland AG
- Murdoch Affiliation
- Information Technology, Mathematics and Statistics
- Language
- English
- Resource Type
- Book chapter
- Additional Information
- Part of the Communications in Computer and Information Science book series (CCIS, volume 1333)
Metrics
177 Record Views