NDLI: A Fast Multi-label Classification Algorithm Based on Double Label Support Vector Machine

Content Provider	IEEE Xplore Digital Library
Author	Jiayang Li Jianhua Xu
Copyright Year	2009
Abstract	Combing one-versus-one decomposition strategy with support vector machine has become an efficient means for multi-label classification problem. But how to speed up its training and test procedures is still a challenging issue. In this paper, we generalize the primary binary support vector machine to construct a double label support vector machine through locating double label instances at marginal region between positive and negative instances, and then design a fast multi-label classification algorithm using the voting rule. Experiments on benchmark datasets Yeast and Scene illustrate that our novel method can be comparable with some existing methods according to some widely used evaluation criteria, and can run faster 17% averagely than the current corresponding method in training procedure.
Starting Page	30
Ending Page	35
File Size	292677
Page Count	6
File Format	PDF
ISBN	9781424454112
e-ISBN	9781424454129
DOI	10.1109/CIS.2009.168
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-11
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Computer science Algorithm design and analysis Fungi Layout Support vector machine classification Classification algorithms SVM Computer security Double Label Computational intelligence Testing
Content Type	Text
Resource Type	Article

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