The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
Incremental Learning of Concept Drift from Streaming Imbalanced Data
Abstract
Learning in nonstationary environments, also known as learning concept drift, is concerned with learning from data whose statistical characteristics change over time. Concept drift is further complicated if the data set is class imbalanced. While these two issues have been independently addressed, their joint treatment has been mostly underexplored. We describe two ensemble-based approaches for learning concept drift from imbalanced data. Our first approach is a logical combination of our previously introduced Learn++.NSE algorithm for concept drift, with the well-established SMOTE for learning from imbalanced data. Our second approach makes two major modifications to Learn++.NSE-SMOTE integration by replacing SMOTE with a subensemble that makes strategic use of minority class data; and replacing Learn++.NSE and its class-independent error weighting mechanism with a penalty constraint that forces the algorithm to balance accuracy on all classes. The primary novelty of this approach is in determining the voting weights for combining ensemble members, based on each classifier's time and imbalance-adjusted accuracy on current and past environments. Favorable results in comparison to other approaches indicate that both approaches are able to address this challenging problem, each with its own specific areas of strength. We also release all experimental data as a resource and benchmark for future research.
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Cites
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Learning from class-imbalanced data: Review of methods and applications
Haixiang, G., Yijing, L., Shang, J., Mingyun, G., Yuanyue, H., & Bing, G. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.12.035 · Cited by 2,377
Ensemble learning for data stream analysis: A survey
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Learning under Concept Drift: A Review
Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., & Zhang, G. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2876857 · Cited by 1,603
A Survey on Ensemble Learning for Data Stream Classification
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Resampling-Based Ensemble Methods for Online Class Imbalance Learning
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Incremental on-line learning: A review and comparison of state of the art algorithms
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A Systematic Study of Online Class Imbalance Learning With Concept Drift
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IncDet: In Defense of Elastic Weight Consolidation for Incremental Object Detection
Liu, L., Kuang, Z., Chen, Y., Xue, J.-H., Yang, W., & Zhang, W. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3002583 · Cited by 90
Incremental learning of concept drift in Multiple Instance Learning for industrial visual inspection
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Metadata
- Authors
- Ditzler, G., & Polikar, R.
- Venue
- IEEE Transactions on Knowledge and Data Engineering
- Published
- 2013-10
- Volume
- 25
- Issue
- 10
- Pages
- 2283-2301
- ISSN
- 1041-4347 (ISSN-L)
- OpenAlex
- W2088220893
- Cited by
- 406
- References
- 46
- FWCI
- 14.57
- Type
- journal article
- Language
- en
- Open access
- closed
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