考虑全过程优化的支持向量机预测方法

摘要

Aiming at the problems about the influence factor choosing, input character set optimizing and parameter optimizing of support vector machine (SVM) prediction, a whole process optimization method is proposed.Firstly, frequency pattern growth association rule is used to analysis and fuzzy Bayesian network combination model and solve the subjectivity problem in influence factor choosing.Then the fuzzy C-means clustering algorithm is improved in dealing with the outlier and optimizing the distance in the clusters and among the clusters to get better input character sets, and this improved method reduces the redundancy and excesses of the training sample sets in the SVM forecasting model.Furthermore, the radial basis function is confirmed as kernel functions in the SVM model by comparing their characters.Modify the particle swarm optimization (PSO) algorithm about the particle speed, location and the inertia weight value and use the modified association PSO model to optimize the SVM parameters and build the forecasting model.Finally, the example shows the proposed method has more accurate prediction precision.%针对支持向量机(support vector machine, SVM)预测过程中影响因素选择、输入特征集优化、核函数选择及参数优化方面存在的问题,提出了一种全过程优化方法.首先使用频繁模式增长关联规则分析和模糊贝叶斯网络组合模型来解决影响因素选择中存在的主观性问题,然后使用在异常值处理和类内距离与类间距离方面进行改进的模糊C均值聚类算法优化输入特征集,减小支持向量机预测模型冗余度及训练样本集过修正度,通过比较各核函数的特点选择径向基核函数作为SVC的核函数,改进了粒子群优化算法中微粒速度和位置函数及惯性权重值算法,使用该方法优化SVM参数并建立预测模型.最后,通过案例运算和分析,证明该文方法具有更高的预测精度.