Campus Units
Electrical and Computer Engineering, Computer Science
Document Type
Article
Publication Version
Submitted Manuscript
Publication Date
2020
Journal or Book Title
arXiv
Abstract
When learning from streaming data, a change in the data distribution, also known as concept drift, can render a previously-learned model inaccurate and require training a new model. We present an adaptive learning algorithm that extends previous drift-detection-based methods by incorporating drift detection into a broader stable-state/reactive-state process. The advantage of our approach is that we can use aggressive drift detection in the stable state to achieve a high detection rate, but mitigate the false positive rate of standalone drift detection via a reactive state that reacts quickly to true drifts while eliminating most false positives. The algorithm is generic in its base learner and can be applied across a variety of supervised learning problems. Our theoretical analysis shows that the risk of the algorithm is competitive to an algorithm with oracle knowledge of when (abrupt) drifts occur. Experiments on synthetic and real datasets with concept drifts confirm our theoretical analysis.
Copyright Owner
The Author(s)
Copyright Date
2020
Language
en
File Format
application/pdf
Recommended Citation
Tahmasbi, Ashraf; Jothimurugesan, Ellango; Tirthapura, Srikanta; and Gibbons, Phillip B., "DriftSurf: A Risk-competitive Learning Algorithm under Concept Drift" (2020). Electrical and Computer Engineering Publications. 243.
https://lib.dr.iastate.edu/ece_pubs/243
Comments
This is a pre-print of the article Tahmasbi, Ashraf, Ellango Jothimurugesan, Srikanta Tirthapura, and Phillip B. Gibbons. "DriftSurf: A Risk-competitive Learning Algorithm under Concept Drift." arXiv preprint arXiv:2003.06508 (2020). Posted with permission.