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Regular Multiple Criteria Linear Programming for Semi-supervised Classification

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3 Author(s)
Zhiquan Qi ; Res. Center on Fictitious Econ. & Data Sci., Beijing, China ; Yingjie Tian ; Yong Shi

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.

Published in:

Data Mining Workshops (ICDMW), 2012 IEEE 12th International Conference on

Date of Conference:

10-10 Dec. 2012

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