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  Feature Selection for Support Vector Machines Using Genetic Algorithms

Fröhlich, H., Chapelle, O., & Schölkopf, B. (2003). Feature Selection for Support Vector Machines Using Genetic Algorithms. In 15th IEEE International Conference on Tools with Artificial Intelligence (pp. 142-148). Piscataway, NJ, USA: IEEE Operations Center. doi:10.1109/TAI.2003.1250182.

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 Creators:
Fröhlich, H1, 2, Author              
Chapelle, O1, 2, Author              
Schölkopf, B1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: 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.

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 Dates: 2003-12
 Publication Status: Published online
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 Rev. Type: -
 Identifiers: BibTex Citekey: 3166
DOI: 10.1109/TAI.2003.1250182
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Title: 15th IEEE International Conference on Tools with Artificial Intelligence 2003
Place of Event: Sacramento, CA, USA
Start-/End Date: 2003-11-05

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Title: 15th IEEE International Conference on Tools with Artificial Intelligence
Source Genre: Proceedings
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Publ. Info: Piscataway, NJ, USA : IEEE Operations Center
Pages: - Volume / Issue: 13 (4) Sequence Number: - Start / End Page: 142 - 148 Identifier: ISBN: 0-7695-2038-3