Computer Engineering and Applications ›› 2007, Vol. 43 ›› Issue (14): 52-55.

• 学术探讨 • Previous Articles     Next Articles

Joint Optimization of Feature Selection and Parameters for Support Vector Regression


  • Received:2006-06-09 Revised:1900-01-01 Online:2007-05-10 Published:2007-05-10


王强 陈英武 邢立宁   

  1. 国防科技大学信息系统与管理学院管理系 国防科技大学人文与管理学院 国防科技大学
  • 通讯作者: 王强

Abstract: In order to improve support vector regression (SVR) learning ability and generalization performance, a joint optimization method for selecting features and SVR parameters is proposed. By using the method, a new set of uncorrelated features is obtained by using principal component analysis , the mean squared error(MSE) is taken into account for the accuracy evaluation of SVR, and a real-coding based immune genetic algorithm is employed to solve the joint optimization problem. Simulation experiments show that the joint optimization method guarantees better regression accuracy and the optimization process has a higher rate than the single optimization of features or SVR parameters.

摘要: 为提高支持向量回归算法的学习能力和泛化性能,提出了特征选择和支持向量回归参数的联合优化方法。联合优化方法采用主成分分析产生新的特征集,以方均误差为目标计算回归精度,并应用实数编码的免疫遗传算法求解此优化问题。仿真实验结果表明,联合优化的回归精度要优于单独优化特征和支持向量回归参数,而且优化速度更快。