Feature Selection for Support Vector Machines by Means of Genetic Algorithms

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	Similar Articles Articles by Holger Fr?hlich Articles by Olivier Chapelle Articles by Bernhard Sch?lkopf

15th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'03)

Sacramento, California, USA

November 03-November 05

ISBN: 0-7695-2038-3

	ASCII Text	x
	Holger Fr?hlich, Olivier Chapelle, Bernhard Sch?lkopf, "Feature Selection for Support Vector Machines by Means of Genetic Algorithms," Tools with Artificial Intelligence, IEEE International Conference on, pp. 142, 15th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'03), 2003.

	BibTex	x
	@article{ 10.1109/TAI.2003.1250182, author = {Holger Fr?hlich and Olivier Chapelle and Bernhard Sch?lkopf}, title = {Feature Selection for Support Vector Machines by Means of Genetic Algorithms}, journal ={Tools with Artificial Intelligence, IEEE International Conference on}, volume = {0}, year = {2003}, issn = {1082-3409}, pages = {142}, doi = {http://doi.ieeecomputersociety.org/10.1109/TAI.2003.1250182}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Tools with Artificial Intelligence, IEEE International Conference on TI - Feature Selection for Support Vector Machines by Means of Genetic Algorithms SN - 1082-3409 SP EP A1 - Holger Fr?hlich, A1 - Olivier Chapelle, A1 - Bernhard Sch?lkopf, PY - 2003 KW - null VL - 0 JA - Tools with Artificial Intelligence, IEEE International Conference on ER -

Holger Fr?hlich, Max-Planck-Institute of Biological Cybernetics

Olivier Chapelle, Max-Planck-Institute of Biological Cybernetics

Bernhard Sch?lkopf, Max-Planck-Institute of Biological Cybernetics

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TAI.2003.1250182

The problem of feature selection is a difficult combinatorial task in Machine Learning and of high practical relevance, e.g. in bioinformatics. Genetic Algorithms (GAs) offer a natural way to solve this problem. In this paper we present a special Genetic Algorithm, which especially takes into account the existing bounds on the generalization error for Support Vector Machines (SVMs). This new approach is compared to the traditional method of performing cross-validation and to other existing algorithms for feature selection.

Citation:

Holger Fr?hlich, Olivier Chapelle, Bernhard Sch?lkopf, "Feature Selection for Support Vector Machines by Means of Genetic Algorithms," ictai, pp.142, 15th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'03), 2003

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