Input Selection for Support Vector Machines Using Genetic Algorithms

2005 ◽  
pp. 544-549
Author(s):  
Hee-Jun Song ◽  
Seon-Gu Lee ◽  
Sung-Hoe Huh
Keyword(s):  
Genetic Algorithms ◽  
Support Vector Machines ◽  
Support Vector ◽  
Input Selection ◽  
Vector Machines ◽  
Selection For

FEATURE SELECTION FOR SUPPORT VECTOR MACHINES USING GENETIC ALGORITHMS

2004 ◽  
Vol 13 (04) ◽  
pp. 791-800 ◽  
Author(s):  
HOLGER FRÖHLICH ◽  
OLIVIER CHAPELLE ◽  
BERNHARD SCHÖLKOPF
Keyword(s):  
Genetic Algorithms ◽  
Feature Selection ◽  
Support Vector Machines ◽  
Cross Validation ◽  
Support Vector ◽  
Generalization Error ◽  
New Approach ◽  
Vector Machines ◽  
Selection For ◽  
Natural Way

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.

Support vector machines and genetic algorithms for detecting unstable angina

2003 ◽  
Author(s):  
J. Sepulveda-Sanchis ◽  
G. Camps-Valls ◽  
E. Soria-Olivas ◽  
S. Salcedo-Sanz ◽  
C. Bousono-Calzon ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Support Vector Machines ◽  
Unstable Angina ◽  
Support Vector ◽  
Vector Machines

Finding an Optimal Combination of Key Training Items Using Genetic Algorithms and Support Vector Machines

2010 ◽  
Vol 9 (4) ◽  
pp. 652-658 ◽  
Author(s):  
Chien-Che Huang ◽  
Ruey-Gwo Chung ◽  
Rong-Chang Chen ◽  
Tung-Shou Chen ◽  
Tzu-Ning Le ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Support Vector Machines ◽  
Optimal Combination ◽  
Support Vector ◽  
Vector Machines
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