Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/8577
Title: AUC maximizing support vector machines with feature selection
Authors: Tian, Y
Shi, Y
Chen, X 
Chen, W
Keywords: AUC
Feature selection
P-norm
Support vector machine
Issue Date: 2011
Source: Procedia computer science, 2011, v. 4, p. 1691-1698 How to cite?
Journal: Procedia Computer Science 
Abstract: In this paper, we proposed a new algorithm, the Sparse AUC maximizing support vector machine, to get more sparse features and higher AUC than standard SVM. By applying p-norm where 0 < p < 1 to the weight w of the separating hyperplane (w · x) + b = 0, the new algorithm can delete less important features corresponding to smaller |w|. Besides, by applying the AUC maximizing objective function, the algorithm can get higher AUC which make the decision function have higher prediction ability. Experiments demonstrate the new algorithm's effectiveness. Some contributions as follows: (1) the algorithm optimizes AUC instead of accuracy; (2) incorporating feature selection into the classification process; (3) conduct experiments to demonstrate the performance.
Description: 11th International Conference on Computational Science, ICCS 2011, Singapore, 1-3 June 2011
URI: http://hdl.handle.net/10397/8577
ISSN: 1877-0509
DOI: 10.1016/j.procs.2011.04.183
Appears in Collections:Conference Paper

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