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[1]朱帮助,林? 健.基于支持向量数据描述的无标签数据多类分类[J].智能系统学报,2009,4(2):131-136.
　ZHU Bang-zhu,LIN Jian.Multiclass classification algorithm for unlabeled data using SVDD[J].CAAI Transactions on Intelligent Systems,2009,4(2):131-136.

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基于支持向量数据描述的无标签数据多类分类

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《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷: 4 期数: 2009年第2期页码: 131-136 栏目: 学术论文—机器学习出版日期: 2009-04-25

Title:: Multiclass classification algorithm for unlabeled data using SVDD

文章编号:: 1673-4785(2009)02-0131-06

作者:: 朱帮助¹, 林? 健²; 1.五邑大学系统科学与技术研究所，广东江门529020；
2.北京航空航天大学经济管理学院，北京100083

Author(s):: ZHU Bang-zhu¹， LIN Jian²; 1.Institute of System Science and Technology, Wuyi University, Jiangmen 529020, China;
2. School of Economics and Management, Beijing University of Aeronautics and Astronautics, Beijing 100083, China

关键词:: 多类分类; 无标签数据; 支持向量数据描述; 主成分分析

Keywords:: multiclass classification; unlabeled data; support vector data description; principle component analysis

分类号:: TP18

文献标志码:: A

摘要:: 为解决支持向量机(SVM)在处理无标签数据多类分类上的难题，提出了一种基于支持向量数据描述(SVDD)的无标签数据多类分类算法.该方法只需要建立一个分类模型就可以实现多类聚类分类.首先采用主成分分析作数据预处理，提取输入数据的统计特征值，得到主成分特征指标输入到SVDD分类器进行多类聚类分类.以珠三角地区物流中心城市分类评价为研究对象，实证结果表明，采用主成分分析降低了数据维度，有效浓缩了评估信息，SVDD分类器很好地区分了各中心城市，实现了多类分类的目的.

Abstract:: Support vector machines (SVM) may encounter problems in dealing with multiclass classification of unlabeled data. So we suggested a new multiclass classification algorithm based on support vector data description (SVDD) in this paper. Compared with other multiclass classification algorithms, the proposed algorithm only needed one classifier to complete the multiclass clustering classification. With this method, principal component analysis (PCA) was used to preprocess original data to extract statistically characteristic values; inputting these values into an SVDD classifier completed multiclass clustering classification. Taking nine cities in the Pearl River delta area as an example, an evaluation was made of the developmental levels of the logistics of these cities. The test results showed that data dimensions were reduced by using principal component analysis, and the evaluated information was effectively concentrated by adopting feature extraction with PCA. Moreover, the SVDD classifier could distinguish the central cities very well, so it can be used as an effective approach for multiclass classification of unlabeled data.

参考文献/References:: ［1］VAPNIK V．Statistical learning theory［M］. New York：Wiley, 1998:5964.?
［2］PLATT J C, CRISTIANINI N, SHAWE T J. Large margin DAGs for multiclass classification［C］//Advances in Neural Information Processing Systems 12.Cambridge,Mass:MIT Press:547553.
［3］WESTON J, WATKINS C. Multiclass support vector machines［R］. CSDTR9804. London： Royal Holloway University,1998.
［4］ZHOU Z H , LI M. Tritraining:exploiting unlabeled data using three classifiers［J］. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(11): 15291541
［5］TAX D M J, DUIN R P W. Support vector domain description［J］. Pattern Recognition Letters, 1999, 20(11/13): 11911199.
［6］TAX D, DUIN R. Outlier detection using classifier instability［J］. Advances in Pattern Recognition, Lecture notes in Computer Science, 1998，1451:593601.
［7］JIANG Shengyi, SONG Xiaoyu, WANG Hui, et al. A clusteringbased method for unsupervised intrusion detections［J］.Pattern Recognition Letters, 2006, 27(7): 802810.
［8］BANERJEE A. A support vector method for anomaly detection in hyperspectral imagery［J］. IEEE Trans on Geoscience and Remote Sensing, 2006, 44(8):22822290
［9］赵学风，段晨东，刘义艳，等.一种基于支持向量数据描述的损伤诊断方法［J］.系统仿真学报, 2008,20(6):15701573.
?ZHAO Xuefeng, DUAN Chendong, LIU Yiyan,et al. Structure damage diagnosis method based on SVDD technique［J］. Journal of System Simulation, 2008,20(6):15701573.
［10］KIM K I, JUNG K, KIM H J. Face recognition using kernel principal component analysis［J］. IEEE Signal Procession Letters，2002, 9(2): 4042.
［11］赵闯，刘凯，李电生.SOFM神经网络在物流中心城〖LL〗市分类评价中的应用［J］.中国公路学报，2004,17(4):119122. 
ZHAO Chuang, LIU Kai, LI Diansheng. Application of SOFM neural network for classification and evaluation of logistics center city［J］.China Journal of Highway and Transport, 2004,17(4):119122.

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备注/Memo

收稿日期：2008-07-12.
基金项目：国家自然科学基金资助项目（70471074）
作者简介：
朱帮助，男，1979年生，讲师，博士，主要研究方向为复杂系统分析与建模、智能信息处理，发表学术论文近20篇，其中多篇被SCI、EI、ISTP收录.
林健，男，1958年生，教授，博士生导师，博士，主要研究方向为复杂系统建模与仿真、信息管理与信息系统，主持多项国家自然科学基金项目和省部级科研项目，发表学术论文150余篇，其中多篇被SCI、EI、ISTP收录.
通信作者：朱帮助.E-mail：wpzbz@126.com.

更新日期/Last Update: 2009-05-04

基于支持向量数据描述的无标签数据多类分类 PDF下载HTML

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基于支持向量数据描述的无标签数据多类分类

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