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http://hdl.handle.net/10397/8577
Title: | AUC maximizing support vector machines with feature selection | Authors: | Tian, Y Shi, Y Chen, X Chen, W |
Issue Date: | 2011 | Source: | Procedia computer science, 2011, v. 4, p. 1691-1698 | 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. | Keywords: | AUC Feature selection P-norm Support vector machine |
Journal: | Procedia Computer Science | ISSN: | 1877-0509 | DOI: | 10.1016/j.procs.2011.04.183 | Description: | 11th International Conference on Computational Science, ICCS 2011, Singapore, 1-3 June 2011 | Rights: | © 2011 Published by Elsevier Ltd. Open access under CC BY-NC-ND license (https://creativecommons.org/licenses/by/3.0/). The following publication Tian, Y., Shi, Y., Chen, X., & Chen, W. (2011). AUC maximizing support vector machines with feature selection. Procedia Computer Science, 4, 1691-1698 is available at https://doi.org/10.1016/j.procs.2011.04.183 |
Appears in Collections: | Conference Paper |
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