2013 IEEE 13th International Conference on Data Mining Workshops (2012)
Brussels, Belgium Belgium
Dec. 10, 2012 to Dec. 10, 2012
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDMW.2012.65
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.
Support vector machines, Training, Accuracy, Laplace equations, Manifolds, Kernel, Linear programming, Basic Endowment Insurance Fund Audit (BEIFA) dataset, semi-supervised classification, Laplacian, RMCLP
Zhiquan Qi, Yingjie Tian, Yong Shi, "Regular Multiple Criteria Linear Programming for Semi-supervised Classification", 2013 IEEE 13th International Conference on Data Mining Workshops, vol. 00, no. , pp. 500-505, 2012, doi:10.1109/ICDMW.2012.65