Feature Selection for Support Vector Machines Using Genetic Algorithms

Fröhlich, H; Chapelle, O; Schölkopf, B

doi:10.1142/S0218213004001818

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Feature Selection for Support Vector Machines Using Genetic Algorithms

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Fröhlich, H
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Chapelle, O
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Schölkopf, B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://www.worldscientific.com/doi/abs/10.1142/S0218213004001818
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Citation

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.

Cite as: https://hdl.handle.net/21.11116/0000-0005-6BBE-E

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.