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。