The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Using additive expert ensembles to cope with concept drift
Abstract
We consider online learning where the target concept can change over time. Previous work on expert prediction algorithms has bounded the worst-case performance on any subsequence of the training data relative to the performance of the best expert. However, because these "experts" may be difficult to implement, we take a more general approach and bound performance relative to the actual performance of any online learner on this single subsequence. We present the additive expert ensemble algorithm AddExp, a new, general method for using any online learner for drifting concepts. We adapt techniques for analyzing expert prediction algorithms to prove mistake and loss bounds for a discrete and a continuous version of AddExp. Finally, we present pruning methods and empirical results for data sets with concept drift.
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Cites
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Cited by
A survey on concept drift adaptation
Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., & Bouchachia, A. (2014) · ACM Computing Surveys · doi:10.1145/2523813 · Cited by 3,735
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
Ensemble learning for data stream analysis: A survey
Krawczyk, B., Minku, L. L., Gama, J., Stefanowski, J., & Woźniak, M. (2017) · Information Fusion · doi:10.1016/j.inffus.2017.02.004 · Cited by 1,050
Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts
Kolter, J. Z., & Maloof, M. A. (2007) · doi:10.5555/1314498.1390333 · Cited by 648
Self-citationA Survey on Ensemble Learning for Data Stream Classification
Gomes, H. M., Barddal, J. P., Enembreck, F., & Bifet, A. (2018) · ACM Computing Surveys · doi:10.1145/3054925 · Cited by 579
Metadata
- Authors
- Kolter, J. Z., & Maloof, M. A.
- Venue
- Proceedings of the 22nd international conference on Machine learning - ICML '05
- Publisher
- ACM Press
- Published
- 2005
- Pages
- 449-456
- OpenAlex
- W2096846143
- Cited by
- 261
- References
- 15
- FWCI
- 7.87
- Type
- conference paper
- Language
- en
- Open access
- closed
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