Abstract
When using traditional support vector data description (SVDD) to deal with classification problems, low accuracy is often achieved, especially in the case of noise interference. The traditional SVDD adopts a fixed hypersphere radius to set classification boundary, which means that it tends to force all normal samples and outliers to be separated by a fixed radius, which is unreasonable to a certain extent. In order to reduce the impact of this defect, a dynamic radius support vector data description (DR-SVDD) is firstly proposed in this paper, which introduces the idea of angle in the kernel space and flexibly selects the relevant decision radius for each testing sample. Then, the feasibility and effectiveness of the proposed algorithm are verified on several benchmark data sets. Finally, DR-SVDD based fault detection is carried out for a certain turboshaft engine, and the expected results are obtained, which fully demonstrates its effectiveness and robustness.
Published Version
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