Journal article
PWIDB: A framework for learning to classify imbalanced data streams with incremental data re-balancing technique
Procedia Computer Science, Vol.176, pp.818-827
2020
Abstract
The performance of classification algorithms with highly imbalanced streaming data depends upon efficient balancing strategy. Some techniques of balancing strategy have been applied using static batch data to resolve the class imbalance problem, which is difficult if applied for massive data streams. In this paper, a new Piece-Wise Incremental Data re-Balancing (PWIDB) framework is proposed. The PWIDB framework combines automated balancing techniques using Racing Algorithm (RA) and incremental rebalancing technique. RA is an active learning approach capable of classifying imbalanced data and can provide a way to select an appropriate re-balancing technique with imbalanced data. In this paper, we have extended the capability of RA for handling imbalanced data streams in the proposed PWIDB framework. The PWIDB accumulates previous knowledge with increments of re-balanced data and captures the concept of the imbalanced instances. The PWIDB is an incremental streaming batch framework, which is suitable for learning with streaming imbalanced data. We compared the performance of PWIDB with a well-known FLORA technique. Experimental results show that the PWIDB framework exhibits an improved and stable performance compared to FLORA and accumulative re-balancing techniques.
Details
- Title
- PWIDB: A framework for learning to classify imbalanced data streams with incremental data re-balancing technique
- Authors/Creators
- R.A. Mohammed (Author/Creator) - Murdoch UniversityK.W. Wong (Author/Creator) - Murdoch UniversityM.F. Shiratuddin (Author/Creator) - Murdoch UniversityX. Wang (Author/Creator) - Murdoch University
- Publication Details
- Procedia Computer Science, Vol.176, pp.818-827
- Publisher
- Elsevier B.V.
- Identifiers
- 991005541338507891
- Copyright
- © 2020 The Author(s)
- Murdoch Affiliation
- Information Technology, Mathematics and Statistics
- Language
- English
- Resource Type
- Journal article
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