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[1]白 蕾,胡 平,苑易伟.基于LD-SVDD的数据分类方法研究[J].工业仪表与自动化装置,2021,(03):80-83.[doi:10.19950/j.cnki.cn61-1121/th.2021.03.016]
　BAI Lei,HU Ping,YUAN Yiwei.Research on data classification method based on LD-SVDD[J].Industrial Instrumentation & Automation,2021,(03):80-83.[doi:10.19950/j.cnki.cn61-1121/th.2021.03.016]
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基于LD-SVDD的数据分类方法研究

《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

卷:
期数:
2021年03期
页码:
80-83
栏目:
出版日期:
2021-06-15

文章信息/Info

Title:
Research on data classification method based on LD-SVDD
作者:
白 蕾1; 2; 胡 平1; 2; 苑易伟3
1. 陕西工业职业技术学院 电气工程学院；
2.咸阳市新能源及微电网系统重点实验室，陕西 咸阳712000；
3.西安理工大学 自动化与信息工程学院，陕西 西安 710048
Author(s):
BAI Lei1; 2;  HU Ping1; 2;  YUAN Yiwei3
1. Electrical Engineering School, Shaanxi Polytechnic Institute；
2. Xianyang Key Laboratory of New Power and Intelligent Microgrid System, Shaanxi Xianyang 712000, China；
3.Faculty of Automation and Information Engineering, Xi’an University of Technology, S
关键词:
支持向量数据描述; 混叠区域; 局部密度
Keywords:
SVDD;  aliasing region;  local density
分类号:
TP391.41
DOI:
10.19950/j.cnki.cn61-1121/th.2021.03.016
文献标志码:
A
摘要:
针对工业过程中数据维数高，导致SVDD算法在建立不同类别数据的超球面时会产生混叠区域的问题，提出了基于近邻密度-支持向量数据描述（LD-SVDD）的数据分类方法。结合局部密度信息在判别数据相似性和SVDD在数据分类的优势，使用SVDD算法对数据进行分类，对分布在混叠区域中的样本采用密度信息进一步判断其类别，通过随机产生的数据集进行仿真，并与SVDD分类结果进行比较，结果表明LD-SVDD的分类准确率提高到了93%。
Abstract:
In order to solve the problem that SVDD algorithm will produce aliasing regions when building hyperspheres of different types of data due to high data dimension in industrial process, a data classification method based on local density support vector data description (LD-SVDD) is proposed. Combining the advantages of local density information in discriminating data similarity and SVDD in data classification, the SVDD algorithm is used to classify the data, and the density information is used to further judge the classification of samples distributed in the aliasing area. The simulation results of randomly generated data sets are compared with the SVDD classification results. The results show that the classification accuracy of LD-SVDD is improved to 93%.

参考文献/References:

[1] TAX DMJ, DUIN RPW. Support vector domain description[J]. Pattern Recognition Letters, 1999, 20 (11–13): 1191-1199.

[2] ATIENCIA R S, WEBER R. Rough-Fuzzy Support Vector Domain Description for outlier detection[C].IEEE International Conference on Fuzzy Systems, Istanbul, Turkey, 2015: 1-6.
[3]张铭钧,吴娟,褚振忠. Multi-fault diagnosis for autonomous underwater vehicle based on fuzzy weighted support vector domain description[J]. China Ocean Engineering, 2014, 28 (5): 599-616.?div>[4] JEONG MK, AN SH,NAM K.SVDD-based financial fraud detection method through respective learnings of normal/abnormal behaviors[J]. International Journal of Security and Its Applications, 2016, 10 (3): 429-437.
[5] LI G, HU Y, CHEN H, et al. A sensor fault detection and diagnosis strategy for screw chiller system using support vector data description-based d-statistic and DV-contribution plots[J].Energy and Buildings, 2016, 133: 230-245.
[6]张敏龙,王涛,王旭平,等.带故障样本的弹性双阈值SVDD在线故障诊断算法及其应用[J].振动工程学报,2016,29(03):555-560.
[7]蔡金燕,杜敏杰.多分类SVDD混叠域识别新方法与故障诊断应用[J].航天控制,2012,30(06):83-88.
[8]祝志博,王培良,宋执环.基于PCA-SVDD的故障检测和自学习辨识[J].浙江大学学报(工学版),2010,44(04):652-658.
[9] LEE KY, KIM DW, LEE KH, et al. Density- induced support vector data description[J]. IEEE Trans Neural Netw, 2007, 18 (1): 284-289.
[10] CAO H , SI G , ZHU W , et al. Enhancing effectiveness of density-based outlier mining[C]. International Symposiums on Information Processing. IEEE Computer Society, 2008.
[11] YUAN Y , CAO H , ZHANG Y , et al. Outlier mining based on neighbor-density-deviation with minimum hyper-sphere[J]. Information Technology & Control, 2016, 45(3):267-277.

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备注/Memo

备注/Memo:
收稿日期：2020-10-12

基金项目：
陕西省教育厅科研计划项目（20JK0802）；
咸阳科技局科研攻关项目(2018k02-10）；
陕西工业职业技术学院院级项目“基于数据挖掘技术的制粉系统故障诊断方法研究与设计”（2020YKYB-051）；
陕西工业职业技术学院院级项目（ZK19-11）

作者简介：
白蕾（1988），女，陕西咸阳，工学硕士，讲师，研究方向为电气自动化、工业机器人及电力电子技术等。
更新日期/Last Update: 1900-01-01