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Front. Inform. Technol. Electron. Eng.  2011, Vol. 12 Issue (2): 83-87    DOI: 10.1631/jzus.C1000022
    
Binary tree of posterior probability support vector machines
Dong-li Wang1,2, Jian-guo Zheng1, Yan Zhou*,2
1 Glorious Sun School of Business and Management, Donghua University, Shanghai 200051, China 2 College of Information Engineering, Xiangtan University, Xiangtan 411105, China
Binary tree of posterior probability support vector machines
Dong-li Wang1,2, Jian-guo Zheng1, Yan Zhou*,2
1 Glorious Sun School of Business and Management, Donghua University, Shanghai 200051, China 2 College of Information Engineering, Xiangtan University, Xiangtan 411105, China
 全文: PDF(123 KB)  
摘要： Posterior probability support vector machines (PPSVMs) prove robust against noises and outliers and need fewer storage support vectors (SVs). Gonen et al. (2008) extended PPSVMs to a multiclass case by both single-machine and multimachine approaches. However, these extensions suffer from low classification efficiency, high computational burden, and more importantly, unclassifiable regions. To achieve higher classification efficiency and accuracy with fewer SVs, a binary tree of PPSVMs for the multiclass classification problem is proposed in this letter. Moreover, a Fisher ratio separability measure is adopted to determine the tree structure. Several experiments on handwritten recognition datasets are included to illustrate the proposed approach. Specifically, the Fisher ratio separability accelerated binary tree of PPSVMs obtains overall test accuracy, if not higher than, at least comparable to those of other multiclass algorithms, while using significantly fewer SVs and much less test time.
关键词： Binary tree;  Support vector machine;  Handwritten recognition;  Classification    
Abstract: Posterior probability support vector machines (PPSVMs) prove robust against noises and outliers and need fewer storage support vectors (SVs). Gonen et al. (2008) extended PPSVMs to a multiclass case by both single-machine and multimachine approaches. However, these extensions suffer from low classification efficiency, high computational burden, and more importantly, unclassifiable regions. To achieve higher classification efficiency and accuracy with fewer SVs, a binary tree of PPSVMs for the multiclass classification problem is proposed in this letter. Moreover, a Fisher ratio separability measure is adopted to determine the tree structure. Several experiments on handwritten recognition datasets are included to illustrate the proposed approach. Specifically, the Fisher ratio separability accelerated binary tree of PPSVMs obtains overall test accuracy, if not higher than, at least comparable to those of other multiclass algorithms, while using significantly fewer SVs and much less test time.
Key words: Binary tree    Support vector machine    Handwritten recognition    Classification
收稿日期: 2010-02-01 出版日期: 2011-02-08
CLC:  TP391  
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Dong-li Wang, Jian-guo Zheng, Yan Zhou. Binary tree of posterior probability support vector machines. Front. Inform. Technol. Electron. Eng., 2011, 12(2): 83-87.

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http://www.zjujournals.com/xueshu/fitee/CN/10.1631/jzus.C1000022    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2011/V12/I2/83

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