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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Chen, Y. W., & Lin, C. J. (2006). Combining SVMs with various feature selection strategies. Studies in Fuzziness and Soft Computing, 207, 315–324. https://doi.org/10.1007/978-3-540-35488-8_13
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