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Handling concept drift via model reuse.
- Source :
- Machine Learning; Mar2020, Vol. 109 Issue 3, p533-568, 36p
- Publication Year :
- 2020
-
Abstract
- In many real-world applications, data are often collected in the form of a stream, and thus the distribution usually changes in nature, which is referred to as concept drift in the literature. We propose a novel and effective approach to handle concept drift via model reuse, that is, reusing models trained on previous data to tackle the changes. Each model is associated with a weight representing its reusability towards current data, and the weight is adaptively adjusted according to the performance of the model. We provide both generalization and regret analysis to justify the superiority of our approach. Experimental results also validate its efficacy on both synthetic and real-world datasets. [ABSTRACT FROM AUTHOR]
- Subjects :
- CONCEPTS
WATER reuse
Subjects
Details
- Language :
- English
- ISSN :
- 08856125
- Volume :
- 109
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- Machine Learning
- Publication Type :
- Academic Journal
- Accession number :
- 142186134
- Full Text :
- https://doi.org/10.1007/s10994-019-05835-w