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A multi-label classification algorithm based on triple class support vector machine

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2 Author(s)
Shu-Peng Wan ; Nanjing Normal Univ., Nanjing ; Jian-Hua Xu

Multi-label classification problem is a special learning task in which its classes are not mutually exclusive and each sample may belong to several classes simultaneously. A novel multi-label classification algorithm based on both one-versus-one decomposition method and triple class support vector machine (SVM) is presented in this paper. One-versus-one decomposition technique is used to pairwise divide a multi-label classification problem into many binary class ones, in which some samples possibly are associated with two labels at the same time. Triple class SVM is a generalization of traditional binary class SVM, where those samples with double labels are considered as a mixed class located between positive and negative classes. Experimental results on benchmark datasets Yeast and Scene demonstrate that our proposed algorithm is comparable with some existed methods, such as rank-SVM, binary-SVM, ML-kNN and etc, according to several evaluation criteria of multi-label learning algorithms.

Published in:

Wavelet Analysis and Pattern Recognition, 2007. ICWAPR '07. International Conference on  (Volume:4 )

Date of Conference:

2-4 Nov. 2007