中文摘要：

针对支持向量机（SVM）中特征选择和参数优化对分类精度有较大影响,提出了一种改进的基于粒子群优化（PSO）的SVM特征选择和参数联合优化算法（GPSO-SVM）,使算法在提高分类精度的同时选取尽可能少的特征数目。为了解决传统粒子群算法在进行优化时易出现陷入局部最优和早熟的问题,该算法在PSO中引入遗传算法（GA）中的交叉变异算子,使粒子在每次迭代更新后进行交叉变异操作来避免这一问题。该算法通过粒子之间的不相关性指数来决定粒子之间的交叉配对,由粒子适应度值的大小决定其变异概率的大小,由此产生新的粒子进入到群体中。这样使得粒子跳出当前搜索到的局部最优位置,提高了群体的多样性,在全局范围内寻找更优值。在不同数据集上进行实验,与基于PSO和GA的特征选择和SVM参数联合优化算法相比,GPSO-SVM的分类精度平均提高了2%~3%,选择的特征数目减少了3%~15%。实验结果表明,所提算法的特征选择和参数优化效果更好。

英文摘要：

In view of feature selection and parameter optimization in Support Vector Machine（ SVM） have great impact on the classification accuracy,an improved algorithm based on Particle Swarm Optimization（ PSO） for SVM feature selection and parameter optimization（ GPSO-SVM） was proposed to improve the classification accuracy and select the number of features as little as possible. In order to solve the problem that the traditional particle swarm algorithm was easy to fall into local optimum and premature maturation,the crossover and mutation operator were introduced from Genetic Algorithm（ GA） that allows the particle to carry out cross and mutation operations after iteration and update to avoid the problem in PSO. The cross matching between particles was determined by the non-correlation index between particles and the mutation probability was determined by the fitness value,thereby new particles was generated into the group. By this way,the particles jump out of the previous search to the optimal position to improve the diversity of the population and to find a better value. Experiments were carried out on different data sets,compared with the feature selection and SVM parameters optimization algorithm based on PSO and GA,the accuracy of GPSOSVM is improved by an average of 2% to 3%,and the number of selected features is reduced by 3% to 15%. The experimental result show that the features selection and parameter optimization of the proposed algorithm are better.