WCSE

Research on Local Neighbor Density Similarity Based SVDD for Data Classification

Abstract— Due to the high dimension of industrial process data, there are aliasing areas when building hyper-spheres of different types of data by SVDD algorithm, which will lead to inaccurate results in condition identification. Focusing on the problem, a method based on local neighbor density similarity support vector data description (LNDS-SVDD) is proposed for data classification. Utilizing the local similar density in discriminating data similarity, the LNDS-SVDD can further judge the classification of samples distributed in the aliasing area using the density information. In this paper, a randomly generated synthetic data set and a real industrial process data set is used for simulating the proposed data classification algorithm. The results show that the algorithm has high classification accuracy, which is an effective condition identification method for the industrial process.

Index Terms— SVDD, aliasing area, local neighbor density similarity, condition identification.

Yiwei Yuan
School of Automation and Information Engineering, Xi'an University of Technology, China; Key Laboratory of Shaanxi Province for Complex System Control and Intelligent Information Processing, China
Kezhuang Liu
School of Automation and Information Engineering, Xi'an University of Technology, China
Yingmin Yi
RSchool of Automation and Information Engineering, Xi'an University of Technology, China
Lei Bai
School of Electrical Engineering, Shaanxi Polytechnic Institute, China

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Cite: Yiwei Yuan, Kezhuang Liu, Yingmin Yi, Lei Bai, " Research on Local Neighbor Density Similarity Based SVDD for Data Classification, " WCSE 2022 Spring Event: 2022 9th International Conference on Industrial Engineering and Applications, pp. 701-706, Sanya, China, April 15-18, 2022.