Study on multi-label text classification based on SVM

Authors:
Yu-ping Qin;Xiu-kun Wang
Affiliations:
College of Information Science and Technology, Bohai University, Jinzhou, China;School of Electronic and Information Engineering, Dalian University of Technology, Dalian, China
Venue:
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
Year:
2009

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Abstract

Two multi-label text classification algorithms are proposed. Firstly, one-against-rest method is used to train sub-classifiers. For the text to be classified, the sub-classifiers are used to obtain the membership vector, and then confirm the classes of the text. Secondly, hyper-sphere support vector machine is used to obtain the smallest hyper-spheres in feature space that contains most texts of the class, which can divide the class texts from others. For the text to be classified, the distances from it to the centre of every hypersphere are used to confirm the classes of the text. The experimental results show that the algorithms have high performance on recall, precision, and F1.