特征选择和支持向量回归参数的联合优化

Joint optimization of feature selection and parameters for support vector regression

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。

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 analysls,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.