Feature selection for support vector machines by means of genetic algorithms

被引：239

作者：

Fröhlich, H ^{[1
]}

Chapelle, O ^{[1
]}

Schölkopf, B ^{[1
]}

机构：

[1] Max Planck Inst Biol Cybernet, Dept Empir Inference, Tubingen, Germany

来源：

15TH IEEE INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE, PROCEEDINGS | 2003年

关键词：

D O I：

10.1109/TAI.2003.1250182

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 [模式识别与智能系统]; 0812 [计算机科学与技术]; 0835 [软件工程]; 1405 [智能科学与技术];

摘要：

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.

引用

页码：142 / 148

页数：7

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