DMRL: A distributed multi-agent reinforcement learning algorithm for imbalanced classification

Traditional imbalanced classification methods rely on sampling or allocating different weights to different classes to improve the recognition rate for minority classes. However, these methods ignore the importance of adaptability, particularly as the degree of imbalance increases, resulting in sign...

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Bibliographic Details

Published in:	Knowledge-based systems Vol. 327; p. 114101
Main Authors:	Ji, Yixin, Jing, Chao
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 09.10.2025
Subjects:	Distributed computing scheme Imbalanced classification Multi-agent Markov decision process Prioritized experience replay Reinforcement learning Multi-agent Markov decision process Imbalanced classification Reinforcement learning Distributed computing scheme Prioritized experience replay
ISSN:	0950-7051
Online Access:	Get full text
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