FEATURE SELECTION FOR SUPPORT VECTOR MACHINES USING GENETIC ALGORITHMS-Reference-Cited by-同舟云学术

FEATURE SELECTION FOR SUPPORT VECTOR MACHINES USING GENETIC ALGORITHMS

Published:2004-12 Issue:04 Volume:13 Page:791-800
ISSN:0218-2130
Container-title:International Journal on Artificial Intelligence Tools
language:en
Short-container-title:Int. J. Artif. Intell. Tools

Author:

FRÖHLICH HOLGER¹,CHAPELLE OLIVIER¹,SCHÖLKOPF BERNHARD¹

Affiliation:

1. Department Empirical Inference, Max-Planck-Institute of Biological Cybernetics, 72076 Tübingen, Germany

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.

Publisher

World Scientific Pub Co Pte Lt

Subject

Artificial Intelligence,Artificial Intelligence

Link

https://www.worldscientific.com/doi/pdf/10.1142/S0218213004001818

Reference16 articles.

1. Adv. in Neural Inf. Proc. Syst. 12;Chapelle O.,2000

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