Two-class support vector data description

Authors:
Guangxin Huang;Huafu Chen;Zhongli Zhou;Feng Yin;Ke Guo
Affiliations:
Key Laboratory for Neuroinformation of Ministry of Education, School of Life Science and Technology, School of Mathematics Science, University of Electronic Science and Technology, Chengdu 610054, ...;Key Laboratory for Neuroinformation of Ministry of Education, School of Life Science and Technology, School of Mathematics Science, University of Electronic Science and Technology, Chengdu 610054, ...;Key Laboratory of Geomathematics of Sichuan Province, School of Information & Management, Chengdu University of Technology, Chengdu 610059, China;School of Science, Sichuan University of Science and Engineering, Zigong 643000, China;Key Laboratory of Geomathematics of Sichuan Province, School of Information & Management, Chengdu University of Technology, Chengdu 610059, China
Venue:
Pattern Recognition
Year:
2011

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Abstract

Support vector data description (SVDD) is a data description method that can give the target data set a spherically shaped description and be used to outlier detection or classification. In real life the target data set often contains more than one class of objects and each class of objects need to be described and distinguished simultaneously. In this case, traditional SVDD can only give a description for the target data set, regardless of the differences between different target classes in the target data set, or give a description for each class of objects in the target data set. In this paper, an improved support vector data description method named two-class support vector data description (TC-SVDD) is presented. The proposed method can give each class of objects in the target data set a hypersphere-shaped description simultaneously if the target data set contains two classes of objects. The characteristics of the improved support vector data descriptions are discussed. The results of the proposed approach on artificial and actual data show that the proposed method works quite well on the 3-class classification problem with one object class being undersampled severely.