Gene selection for cancer classification using bootstrapped genetic algorithms and support vector machines

Author(s):  
X.-W. Chen
IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 64895-64905
Author(s):  
Essam H. Houssein ◽  
Diaa Salama Abdelminaam ◽  
Hager N. Hassan ◽  
Mustafa M. Al-Sayed ◽  
Emad Nabil

2004 ◽  
Vol 13 (04) ◽  
pp. 791-800 ◽  
Author(s):  
HOLGER FRÖHLICH ◽  
OLIVIER CHAPELLE ◽  
BERNHARD SCHÖLKOPF

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.


Author(s):  
J. Sepulveda-Sanchis ◽  
G. Camps-Valls ◽  
E. Soria-Olivas ◽  
S. Salcedo-Sanz ◽  
C. Bousono-Calzon ◽  
...  

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