Abstract
This article investigates the performance of combining support vector machines (SVM) and various feature selection strategies. Some of them are filter-type approaches: general feature selection methods independent of SVM, and some are wrapper-type methods: modifications of SVM which can be used to select features. We apply these strategies while participating to the NIPS 2003 Feature Selection Challenge and rank third as a group.
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References
B. Boser, I. Guyon, and V. Vapnik. A training algorithm for optimal margin classifiers. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pages 144–152, 1992.
Leo Breiman. Random forests. Machine Learning, 45(1):5–32, 2001. URL citeseer. nj.nec.com/breiman01random.html.
Chih-Chung Chang and Chih-Jen Lin. LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
O. Chapelle, V. Vapnik, O. Bousquet, and S. Mukherjee. Choosing multiple parameters for support vector machines. Machine Learning, 46:131–159, 2002.
W. Chu, S.S. Keerthi, and C.J. Ong. Bayesian trigonometric support vector classifier. Neural Computation, 15(9):2227–2254, 2003.
Kai-Min Chung, Wei-Chun Kao, Chia-Liang Sun, Li-Lun Wang, and Chih-Jen Lin. Radius margin bounds for support vector machines with the RBF kernel. Neural Computation, 15:2643–2681, 2003.
C. Cortes and V. Vapnik. Support-vector network. Machine Learning, 20:273–297, 1995.
Yann LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324, November 1998. MNIST database available at http://yann.lecun.com/exdb/mnist/.
Andy Liaw and Matthew Wiener. Classification and regression by randomForest. R News, 2/3:18–22, December 2002. URL http://cran.r-project.org/doc/Rnews/Rnews 2002-3.pdf.
V. Svetnik, A. Liaw, C. Tong, and T. Wang. Application of Breiman’s random forest to modeling structure-activity relationships of pharmaceutical molecules. In F. Roli, J. Kittler, and T. Windeatt, editors, Proceedings of the 5th International Workshopon Multiple Classifier Systems, Lecture Notes in Computer Science vol. 3077., pages 334–343. Springer, 2004.
Vladimir Vapnik. Statistical Learning Theory. Wiley, New York, NY, 1998.
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Chen, YW., Lin, CJ. (2006). Combining SVMs with Various Feature Selection Strategies. In: Guyon, I., Nikravesh, M., Gunn, S., Zadeh, L.A. (eds) Feature Extraction. Studies in Fuzziness and Soft Computing, vol 207. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-35488-8_13
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DOI: https://doi.org/10.1007/978-3-540-35488-8_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35487-1
Online ISBN: 978-3-540-35488-8
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