Unsupervised domain adaptation by incremental learning for concept drifting data streams

Incremental learning is a learning paradigm in which a model is updated continuously as new data becomes available, and its main challenge is to adapt to non-stationary environments without the time-consuming re-training process. Many efforts have been made on incremental supervised learning. Howeve...

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Bibliographic Details
Published in:International journal of machine learning and cybernetics 2024-09, Vol.15 (9), p.4055-4078
Main Authors: Moradi, Mona, Rahmanimanesh, Mohammad, Shahzadi, Ali
Format: Article
Language:English
Subjects:
Adaptation
Artificial Intelligence
Complex Systems
Computational Intelligence
Control
Data transmission
Decision making
Drift
Engineering
Knowledge
Learning
Mechatronics
Methods
Nonstationary environments
Original Article
Pattern Recognition
Robotics
Supervised learning
Systems Biology
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Summary:Incremental learning is a learning paradigm in which a model is updated continuously as new data becomes available, and its main challenge is to adapt to non-stationary environments without the time-consuming re-training process. Many efforts have been made on incremental supervised learning. However, providing sufficient labeled data remains a major problem. Recently, domain adaptation methods have gained attention. These methods aim to leverage the knowledge from an auxiliary source domain to boost the performance of the model in the target domain by reducing the domain discrepancy between them. Regarding these issues, in the present paper, a proposed model aims to incrementally learn a new domain characterized by drifts due to a non-stationary environment. It utilizes an unsupervised, fuzzy-based domain adaptation to classify data streams faced with concept drift while accounting for a label-agnostic incremental setting in the target domain. Incremental learning updates occur whenever the entropy-based metric indicates uncertainty, ensuring informative samples are integrated. Also, outdated samples are forgotten during the training stage using the dynamic sample weighting strategy. Through experimentation on forty-five tasks, the superiority of the proposed model in handling dynamic adaptation on non-stationary domains is demonstrated, showcasing improvements in accuracy and computational efficiency.
ISSN:1868-8071
1868-808X
DOI:10.1007/s13042-024-02135-1