The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Dynamic weighted majority: a new ensemble method for tracking concept drift
Abstract
Algorithms for tracking concept drift are important for many applications. We present a general method based on the weighted majority algorithm for using any online learner for concept drift. Dynamic weighted majority (DWM) maintains an ensemble of base learners, predicts using a weighted-majority vote of these "experts", and dynamically creates and deletes experts in response to changes in performance. We empirically evaluated two experimental systems based on the method using incremental naive Bayes and incremental tree inducer [ITI] as experts. For the sake of comparison, we also included Blum's implementation of weighted majority. On the STAGGER concepts and on the SEA concepts, results suggest that the ensemble method learns drifting concepts almost as well as the base algorithms learn each concept individually. Indeed, we report the best overall results for these problems to date.
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Cites
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Popular Ensemble Methods: An Empirical Study
Opitz, D., & Maclin, R. (1999) · Journal of Artificial Intelligence Research · doi:10.1613/jair.614 · Cited by 2,953
On the Quasi-Minimal Solution of the General Covering Problem
Michalski, R. S. (1969) · George Mason University · doi:10.13021/mars/3596 · Cited by 232
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
Sagi, O., & Rokach, L. (2018) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1249 · Cited by 3,103
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
Cross-domain video concept detection using adaptive svms
Yang, J., Yan, R., & Hauptmann, A. G. (2007) · Proceedings of the 15th ACM international conference on Multimedia · doi:10.1145/1291233.1291276 · Cited by 667
A 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
- Third IEEE International Conference on Data Mining
- Publisher
- IEEE Comput. Soc
- Published
- 2004-04-23
- Pages
- 123-130
- ISSN
- 1550-4786 (ISSN-L)
- OpenAlex
- W2110222014
- Cited by
- 419
- References
- 3
- FWCI
- 10.19
- Type
- conference paper
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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