基于微分进化算法的支持向量机参数与特征同步选择

为了提高支持向量机(SVM)参数选择和特征选择的效率,提出了一种基于微分进化(DE)算法的SVM参数与特征同步选择方法(DE-SVM)。在编码方式上将DE的个体分为参数维和特征维,参数维直接用于优化选择参数,特征维经过"取整二进制变换"后选择相应特征。在几个UCI标准数据集上的仿真试验证明了该方法的有效性,与基于微粒群算法的参数与特征同步选择方法(PSO-SVM)相比,DE-SVM具有更高的寻优效率和特征选择能力。

Simultaneous selection of parameters and features for SVM based on the differential evolution algorithm

To improve the efficiency of SVM's parameters selection and features selection,a simultaneous selection method based on the differential evolution algorithm(DE-SVM)was proposed. In the coding mode,the vectors of DE are divided into parameter dimensions and feature dimensions. The parameter dimensions are used directly to select the parameters,and by"integer-binary conversion"the feature dimensions are used to select the features.Several numerical experiments on UCI benchmark datasets show the effectiveness ...