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Tracking Correlations Between Multiple Data Streams Through Evolutionary Regressor Chains
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AI
DOI：10.1109/TCYB.2025.3587025.png)
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Abstract
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In a real-world setting, several correlational data streams are active at once. An essential question is how to use the correlations between data streams to enhance the effectiveness of machine learning models. The fact that data streams are nonstationary and the correlations across data streams might change over time presents another difficulty. We suggest an ensemble chain-structured model, Evolutionary regressor chains (RCs), to track the correlations between data streams to solve these issues. We develop a heuristic order searching approach to search for the chain’s optimal order. With the ability to monitor the dynamicity of the correlations, the heuristic order searching technique can also update the chains over time. Furthermore, a way for reducing computing complexity while maintaining the ensemble’s diversity is proposed. The method’s theoretical foundation is established through a dynamic regret analysis proving optimal adaptation in the data streams. The outcomes of our experiments demonstrate the effectiveness of Evolutionary RCs.
Keywords:
Concept drift
multioutput learning
online learning
Journal
IF:
10.5
Papers:
1.1W
Citations:
5.0W
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Cited Papers
Multi-target regression via input space expansion: treating targets as inputs.png)
MACHINE LEARNING
OAAI
MACHINE LEARNINGIF2.9
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