The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
DDD: A New Ensemble Approach for Dealing with Concept Drift
Abstract
Online learning algorithms often have to operate in the presence of concept drifts. A recent study revealed that different diversity levels in an ensemble of learning machines are required in order to maintain high generalization on both old and new concepts. Inspired by this study and based on a further study of diversity with different strategies to deal with drifts, we propose a new online ensemble learning approach called Diversity for Dealing with Drifts (DDD). DDD maintains ensembles with different diversity levels and is able to attain better accuracy than other approaches. Furthermore, it is very robust, outperforming other drift handling approaches in terms of accuracy when there are false positive drift detections. In all the experimental comparisons we have carried out, DDD always performed at least as well as other drift handling approaches under various conditions, with very few exceptions.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy
Kuncheva, L. I., & Whitaker, C. J. (2003) · Machine Learning · doi:10.1023/a:1022859003006 · Cited by 2,335
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
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
A survey on data preprocessing for data stream mining: Current status and future directions
Ramírez-Gallego, S., Krawczyk, B., García, S., Woźniak, M., & Herrera, F. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.01.078 · Cited by 502
Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects
Angelopoulos, A., Michailidis, E. T., Nomikos, N., Trakadas, P., Hatziefremidis, A., Voliotis, S., & Zahariadis, T. (2019) · Sensors · doi:10.3390/s20010109 · Cited by 365
Sturm, F., Trat, M., Sathiyababu, R., Allipilli, H., Menz, B., Hergenroether, E., & Siegel, M. (2025) · Machine Vision and Applications · doi:10.1007/s00138-024-01638-9 · Cited by 2
Metadata
- Authors
- Minku, L. L., & Yao, X.
- Venue
- IEEE Transactions on Knowledge and Data Engineering
- Published
- 2012-04
- Volume
- 24
- Issue
- 4
- Pages
- 619-633
- ISSN
- 1041-4347 (ISSN-L)
- OpenAlex
- W2123003172
- Cited by
- 431
- References
- 1
- FWCI
- 18.33
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
