The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Online and Non-Parametric Drift Detection Methods Based on Hoeffding’s Bounds
Open accessAbstract
Incremental and online learning algorithms are more relevant in the data mining context because of the increasing necessity to process data streams. In this context, the target function may change overtime, an inherent problem of online learning (known as concept drift). In order to handle concept drift regardless of the learning model, we propose new methods to monitor the performance metrics measured during the learning process, to trigger drift signals when a significant variation has been detected. To monitor this performance, we apply some probability inequalities that assume only independent, univariate and bounded random variables to obtain theoretical guarantees for the detection of such distributional changes. Some common restrictions for the online change detection as well as relevant types of change (abrupt and gradual) are considered. Two main approaches are proposed, the first one involves moving averages and is more suitable to detect abrupt changes. The second one follows a widespread intuitive idea to deal with gradual changes using weighted moving averages. The simplicity of the proposed methods, together with the computational efficiency make them very advantageous. We use a Naive Bayes classifier and a Perceptron to evaluate the performance of the methods over synthetic and real data.
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Cited by
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
Ahmadi‐Assalemi, G., Al‐Khateeb, H., Benson, V., Adamyk, B., & Ammi, M. (2025) · IET Cyber-Physical Systems: Theory & Applications · doi:10.1049/cps2.70004 · Cited by 10
Metadata
- Authors
- Frias-Blanco, I., Campo-Avila, J. D., Ramos-Jimenez, G., Morales-Bueno, R., Ortiz-Diaz, A., & Caballero-Mota, Y.
- Venue
- IEEE Transactions on Knowledge and Data Engineering
- Published
- 2015-03-01
- Volume
- 27
- Issue
- 3
- Pages
- 810-823
- ISSN
- 1041-4347 (ISSN-L)
- OpenAlex
- W2097178527
- Cited by
- 469
- References
- 24
- FWCI
- 8.91
- Type
- journal article
- Language
- en
- Open access
- green
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- Frias-Blanco, I., Campo-Avila, J. D., Ramos-Jimenez, G., Morales-Bueno, R., Ortiz-Diaz, A., & Caballero-Mota, Y. (2015). Online and Non-Parametric Drift Detection Methods Based on Hoeffding’s Bounds. IEEE Transactions on Knowledge and Data Engineering. https://doi.org/10.1109/tkde.2014.2345382
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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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