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2012 IEEE 12th International Conference on Data Mining Workshops
Regular Multiple Criteria Linear Programming for Semi-supervised Classification
Brussels, Belgium Belgium
December 10-December 10
ISBN: 978-1-4673-5164-5
In this paper, inspired by the application potential of Regular Multiple Criteria Linear Programming (RMCLP), we proposed a novel Laplacian RMCLP(called Lap-RMCLP)method for semi-supervised classification problem, which can exploit the geometry information of the marginal distribution embedded in unlabeled data to construct a more reasonable classifier and is a useful extension of TSVM. Furthermore, by adjusting the parameter, Lap-RMCLP can convert to RMCLP naturally. All experiments on public and data sets and Basic Endowment Insurance Fund Audit(BEIFA) dataset show that Lap-RMCLP is a competitive method in semi-supervised classification.
Index Terms:
Support vector machines,Training,Accuracy,Laplace equations,Manifolds,Kernel,Linear programming,Basic Endowment Insurance Fund Audit (BEIFA) dataset,semi-supervised classification,Laplacian,RMCLP
Citation:
Zhiquan Qi, Yingjie Tian, Yong Shi, "Regular Multiple Criteria Linear Programming for Semi-supervised Classification," icdmw, pp.500-505, 2012 IEEE 12th International Conference on Data Mining Workshops, 2012
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