Skip to main content

Automated Adaptation Strategies for Stream Learning.

Bakirov, R., Gabrys, B. and Fay, D., 2021. Automated Adaptation Strategies for Stream Learning. arXiv (1812.10793v3 [cs.LG]).

Full text available as:

[img]
Preview
PDF (arXiv preprint)
1812.10793v3.pdf - Published Version
Available under License Creative Commons Attribution Non-commercial.

981kB

Official URL: https://arxiv.org/abs/1812.10793

Abstract

Automation of machine learning model development is increasingly becoming an established research area. While automated model selection and automated data pre-processing have been studied in depth, there is, however, a gap concerning automated model adaptation strategies when multiple strategies are available. Manually developing an adaptation strategy can be time consuming and costly. In this paper we address this issue by proposing the use of flexible adaptive mechanism deployment for automated development of adaptation strategies. Experimental results after using the proposed strategies with five adaptive algorithms on 36 datasets confirm their viability. These strategies achieve better or comparable performance to the custom adaptation strategies and the repeated deployment of any single adaptive mechanism.

Item Type:Article
ISSN:1524-0703
Uncontrolled Keywords:cs.LG ; cs.LG ; stat.ML
Group:Faculty of Science & Technology
ID Code:35639
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:14 Jun 2021 14:35
Last Modified:15 Aug 2021 08:29

Downloads

Downloads per month over past year

More statistics for this item...
Repository Staff Only -