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Recognition of cobalt-rich crusts based on multi-classifier fusion in seafloor mining environments
In seafloor mining environments, ultrasonic detection is often used for underwater target recognition, and a large number of suspended particles reduce the recognition rate of single-classifier cobalt-rich crusts. In this paper, we propose a recognition method based on multi-classifier fusion (MCF). First, the kernel Fisher discriminant analysis (KFDA) method is used to reduce the multi-class feature dimension of the signal, which improves the computational efficiency. Then, the probabilistic neural network (PNN), support vector data description (SVDD), and K-nearest neighbors (KNN) classifier are designed, and the input features of the different classifiers are selected by the genetic algorithm (GA) to improve the recognition rate of classifiers. Finally, the basic probability assignment (BPA) is recalculated in combination with the accuracy of the classifier, and conflicting evidence is synthesized to realize the MCF decision. The experimental results indicate that MCF recognition surpasses single classifier recognition. The Dempster-Shafer (D-S) theory MCF identification method proposed in this paper can be effectively applied to the identification of cobalt-rich crusts in seafloor mining environments.
Recognition of cobalt-rich crusts based on multi-classifier fusion in seafloor mining environments
In seafloor mining environments, ultrasonic detection is often used for underwater target recognition, and a large number of suspended particles reduce the recognition rate of single-classifier cobalt-rich crusts. In this paper, we propose a recognition method based on multi-classifier fusion (MCF). First, the kernel Fisher discriminant analysis (KFDA) method is used to reduce the multi-class feature dimension of the signal, which improves the computational efficiency. Then, the probabilistic neural network (PNN), support vector data description (SVDD), and K-nearest neighbors (KNN) classifier are designed, and the input features of the different classifiers are selected by the genetic algorithm (GA) to improve the recognition rate of classifiers. Finally, the basic probability assignment (BPA) is recalculated in combination with the accuracy of the classifier, and conflicting evidence is synthesized to realize the MCF decision. The experimental results indicate that MCF recognition surpasses single classifier recognition. The Dempster-Shafer (D-S) theory MCF identification method proposed in this paper can be effectively applied to the identification of cobalt-rich crusts in seafloor mining environments.
Recognition of cobalt-rich crusts based on multi-classifier fusion in seafloor mining environments
Hu, Gang (author) / Zhao, Haiming (author) / Han, Fenglin (author) / Wang, Yanli (author)
Marine Georesources & Geotechnology ; 39 ; 1205-1214
2021-10-03
10 pages
Article (Journal)
Electronic Resource
Unknown
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