[1]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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Multiclass classification algorithm for unlabeled data using SVDD

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 4 Number of periods: 2009 2 Page number: 131-136 Column: 学术论文—机器学习 Public date: 2009-04-25
Title:
Multiclass classification algorithm for unlabeled data using SVDD
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
Keywords:
multiclass classification;  unlabeled data;  support vector data description;  principle component analysis
CLC:
TP18
DOI:
-
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.
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