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  Feature Selection for Support Vector Machines Using Genetic Algorithms

Fröhlich, H., Chapelle, O., & Schölkopf, B. (2004). Feature Selection for Support Vector Machines Using Genetic Algorithms. International Journal on Artificial Intelligence Tools, 13(4), 791-800. doi:10.1142/S0218213004001818.

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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, 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: 2004-12
 Publication Status: Published in print
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 Identifiers: DOI: 10.1142/S0218213004001818
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Title: International Journal on Artificial Intelligence Tools
Source Genre: Journal
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Pages: - Volume / Issue: 13 (4) Sequence Number: - Start / End Page: 791 - 800 Identifier: ISSN: 0218-2130
CoNE: https://pure.mpg.de/cone/journals/resource/958480166376