Document Type
Article
Department or Administrative Unit
Computer Science
Publication Date
10-15-2021
Abstract
Data classification in streams where the underlying distribution changes over time is known to be difficult. This problem—known as concept drift detection—involves two aspects: (i) detecting the concept drift and (ii) adapting the classifier. Online training only considers the most recent samples; they form the so-called shifting window. Dynamic adaptation to concept drift is performed by varying the width of the window. Defining an online Support Vector Machine (SVM) classifier able to cope with concept drift by dynamically changing the window size and avoiding retraining from scratch is currently an open problem. We introduce the Adaptive Incremental–Decremental SVM (AIDSVM), a model that adjusts the shifting window width using the Hoeffding statistical test. We evaluate AIDSVM performance on both synthetic and real-world drift datasets. Experiments show a significant accuracy improvement when encountering concept drift, compared with similar drift detection models defined in the literature. The AIDSVM is efficient, since it is not retrained from scratch after the shifting window slides.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Gâlmeanu, H., & Andonie, R. (2021). Concept Drift Adaptation with Incremental–Decremental SVM. Applied Sciences, 11(20), 9644. https://doi.org/10.3390/app11209644
Journal
Applied Sciences
Rights
© 2021 by the authors.
Comments
This article was originally published Open Access in Applied Sciences. The full-text article from the publisher can be found here.