Title :
Notice of Retraction
A GA-based feature selection and parameters optimization for support vector regression
Author :
Lei Li ; Yang Duan
Author_Institution :
Coll. of Sci., Nanjing Univ. of Posts & Telecommun., Nanjing, China
Abstract :
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
The regression analysis is a method in mathematical statistics to solve many practical problem. Support Vector Regression (SVR) is an effective method for resolving regression problem. However, the traditional SVR impose many of the limitations, the SVR parameters need optimizing, but there is not a mature theoretic for choosing the parameters of SVR, which causes much discommodity to the appliance of SVR. This paper proposes and investigates the use of a genetic algorithm approach for simultaneously select an optimal feature subset and optimize SVR parameters.
Keywords :
genetic algorithms; regression analysis; support vector machines; GA-based feature selection; SVR parameter optimization; genetic algorithm; mathematical statistics; optimal feature subset; parameters optimization; regression analysis; support vector regression; Biological cells; Genetic algorithms; Kernel; Optimization; Support vector machines; Training; Feature Selection; Genetic Algorithm; Parameters Optimization; Support Vector Regression(SVR);
Conference_Titel :
Natural Computation (ICNC), 2011 Seventh International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-9950-2
DOI :
10.1109/ICNC.2011.6022110
