Abstract
Convex hull is one of the basic means to describe the shape of objects, which is used in many fields of computer graphics and images. This paper proposes a support vector machine classification method based on convex hull clipping. Principle component analysis (PCA) is used for pretreatment on the planar point set. The end point of convex hull is determined by the PCA fitting line, then convex hull is obtained by the rapid sorting method. During classification, the approximating line of the boundary is constructed by translating the fitting line to the class boundary. Samples in the region surrounded by the approximating line and convex hull are boundary samples, which are used to the train support vector machine (SVM). Experiments on two artificial data sets and the UCI standard data set show that the proposed method can improve classification accuracy and reduce training time, especially when the training set is large.
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Acknowledgments
This work was financially supported by Nantong Science and Technology Project (GCZ19048), and supported by Nantong Polytechnic College Young and Middle-aged Scientific Research Training Project (ZQNGG209), and supported by Nantong Key Laboratory of intelligent control and intelligent Computing, and by supported by Nantong Science and Technology Project (GC2019130).
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Guo, Y., Wu, J. (2021). Support Vector Machine Classification Method Based on Convex Hull Clipping. In: Fei, M., Chen, L., Ma, S., Li, X. (eds) Intelligent Life System Modelling, Image Processing and Analysis. LSMS ICSEE 2021 2021. Communications in Computer and Information Science, vol 1467. Springer, Singapore. https://doi.org/10.1007/978-981-16-7207-1_11
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DOI: https://doi.org/10.1007/978-981-16-7207-1_11
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