Feature Selection for Support Vector Machines Base on Modified Artificial Fish Swarm Algorithm

2015 ◽  
pp. 297-304 ◽  
Author(s):  
Kuan-Cheng Lin ◽  
Sih-Yang Chen ◽  
Jason C. Hung
Keyword(s):  
Feature Selection ◽  
Support Vector Machines ◽  
Support Vector ◽  
Artificial Fish Swarm Algorithm ◽  
Vector Machines ◽  
Artificial Fish Swarm ◽  
Selection For ◽  
Swarm Algorithm

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.

Feature selection for support vector machines with RBF kernel

2011 ◽  
Vol 36 (2) ◽  
pp. 99-115 ◽  
Author(s):  
Quanzhong Liu ◽  
Chihau Chen ◽  
Yang Zhang ◽  
Zhengguo Hu
Keyword(s):  
Feature Selection ◽  
Support Vector Machines ◽  
Support Vector ◽  
Vector Machines ◽  
Rbf Kernel ◽  
Selection For
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