A combined parallel genetic algorithm and support vector machine model for breast cancer detection

2017 ◽  
Vol 16 (4) ◽  
pp. 773-785 ◽  
Author(s):  
Hongyan Xu ◽  
Ting Chen ◽  
Junmin Lv ◽  
Jin Guo
Keyword(s):  
Breast Cancer ◽  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Cancer Detection ◽  
Support Vector Machine Model ◽  
Breast Cancer Detection ◽  
Support Vector ◽  
Parallel Genetic Algorithm ◽  
Machine Model

Thermography Based Breast Cancer Detection Using Texture Features and Support Vector Machine

2010 ◽  
Vol 36 (3) ◽  
pp. 1503-1510 ◽  
Author(s):  
U. Rajendra Acharya ◽  
E. Y. K. Ng ◽  
Jen-Hong Tan ◽  
S. Vinitha Sree
Keyword(s):  
Breast Cancer ◽  
Support Vector Machine ◽  
Cancer Detection ◽  
Texture Features ◽  
Breast Cancer Detection ◽  
Support Vector

Parameters of Support Vector Machines Model Optimized Method Based on Genetic Algorithm

2014 ◽  
Vol 989-994 ◽  
pp. 1873-1876
Author(s):  
Yu Zhen Xie ◽  
Zhao Gang Wang ◽  
Xiao Wei Dai
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Support Vector Machine Model ◽  
Optimization Problems ◽  
Support Vector ◽  
Machine Model ◽  
Penalty Factor ◽  
Vector Machines ◽  
Cross Validation Error ◽  
Function Selection

In order to obtain more accurate parameters of support vector machine model, using genetic algorithm to optimize the parameters is an effective method. This paper analyzes the principle of support vector machine for regression, support vector machine kernel function selection, kernel parameters, penalty factor selection and adjustment methods, taking into account genetic algorithm is effective in solving optimization problems, proposed a method using genetic algorithm to optimize the parameters of support vector machine, which uses genetic algorithms to make cross-validation error minimized. The simulation results demonstrate the effectiveness of this method.

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