Resampling-based ensemble methods for online class imbalance learning
- Submitting institution
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The University of Birmingham
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 54722116
- Type
- D - Journal article
- DOI
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10.1109/TKDE.2014.2345380
- Title of journal
- IEEE Transactions on Knowledge and Data Engineering
- Article number
- -
- First page
- 1356
- Volume
- 27
- Issue
- 5
- ISSN
- 1041-4347
- Open access status
- Out of scope for open access requirements
- Month of publication
- August
- Year of publication
- 2014
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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2
- Research group(s)
-
-
- Citation count
- 133
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Class imbalance is a challenging problem that can deteriorate predictive performance of classifiers. This issue is exacerbated in online data stream learning, as the imbalance ratio is unknown beforehand and can change over time. This work proposed the first online class imbalanced learning approach able to cope with changes in the imbalance ratio. Empirical results showed better accuracy and robustness than existing algorithms, through factorial ANOVA. This paper was published in a top machine learning journal, cited by world-leading groups (eg, Porto University, Politecnico di Milano), and included in our WCCI’18 tutorial which attracted 50+ attendees.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -