Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/31938
Title: Feature subset selection for support vector machines through sensitivity analysis
Authors: Wang, D
Chan, PPK
Yeung, DS
Tsang, ECC
Keywords: Feature extraction
Generalisation (artificial intelligence)
Pattern classification
Sensitivity analysis
Set theory
Support vector machines
Issue Date: 2004
Publisher: IEEE
Source: Proceedings of 2004 International Conference on Machine Learning and Cybernetics, 2004, 26-29 August 2004, v. 7, p. 4257-4262 How to cite?
Abstract: In the context of support vector machines, feature selection is motivated mainly by the consideration of classification speed and generalization ability. Sensitivity analysis of MLP and RBF has already been successfully applied in feature subset selection. We present a novel feature selection method for support vector machines (SVMs) using the sensitivity analysis of SVMs, which is defined as the deviation of separation margin with respect to the perturbation of given feature. The method we proposed can directly be applied to multi-class SVMs. Our experiments validate that the proposed strategy produces satisfactory results both on artificial and real-world data.
URI: http://hdl.handle.net/10397/31938
ISBN: 0-7803-8403-2
DOI: 10.1109/ICMLC.2004.1384586
Appears in Collections:Conference Paper

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