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Ensemble learning based multi-source information fusion
Author(s): Junyi Xu; Le Li; Ming Ji
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Paper Abstract

Aiming at the target recognition tasks of multi-source sensors, this paper proposes a decision-level information fusion model based on ensemble learning to improve the target recognition ability of distributed sensors. Based on distributed sensing data, feature analysis model is first constructed to reduce the dimension of original data. Then, target recognition model is constructed by data mining to realize the rapid identification by single classifier. On this basis, information fusion model based on ensemble learning is proposed to assist decision-making, combined with different ensemble strategies to improve the robustness and reliability of multi-source sensor target recognition. Finally, five public data sets are used to verify the effect of multi-source information fusion model under four homogeneous strategies and two heterogeneous strategies.

Paper Details

Date Published: 27 November 2019
PDF: 9 pages
Proc. SPIE 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence, 1132123 (27 November 2019); doi: 10.1117/12.2542941
Show Author Affiliations
Junyi Xu, Academy of Military Science (China)
Le Li, Audit Office of Central Military Commission (China)
Ming Ji, Academy of Military Science (China)


Published in SPIE Proceedings Vol. 11321:
2019 International Conference on Image and Video Processing, and Artificial Intelligence
Ruidan Su, Editor(s)

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