Multi-Stream Concept Drift Self-Adaptation Using Graph Neural Network-Reference-Cited by-同舟云学术

Multi-Stream Concept Drift Self-Adaptation Using Graph Neural Network

Published:2023-12-01 Issue:12 Volume:35 Page:12828-12841
ISSN:1041-4347
Container-title:IEEE Transactions on Knowledge and Data Engineering
language:
Short-container-title:IEEE Trans. Knowl. Data Eng.

Author:

Zhou Ming¹^ORCID,Lu Jie¹^ORCID,Song Yiliao¹^ORCID,Zhang Guangquan¹^ORCID

Affiliation:

1. Decision Systems and e-Service Intelligence Laboratory, Australian Artificial Intelligence Institute (AAII), Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, Australia

Funder

Australian Research Council

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Computer Science Applications,Information Systems

Link

http://xplorestaging.ieee.org/ielx7/69/10311056/10115471.pdf?arnumber=10115471

Reference47 articles.

1. A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streams

2. Dynamic graph convolutional recurrent network for traffic prediction: Benchmark and solution;li;ACM Trans Knowl Discov Data,2021

3. A Framework for Multistream Regression With Direct Density Ratio Estimation

4. GMAN: A Graph Multi-Attention Network for Traffic Prediction

5. Multistream regression with asynchronous concept drift detection

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