基于特征指数加权的最小二乘支持向量机算法

根据支持向量回归机原理,针对样本特征对回归预测重要性的差异,采用最小二乘支持向量回归机(LS-SVR)算法,减少参数数量,针对参数对预测效果的影响,并考虑到特征加权的意义,采用特征指数进行加权,其权重系数由灰色关联度确定,提出了基于特征指数加权的最小二乘支持向量回归机算法。为验证该算法的有效性,对实际股票价格进行预测,结果表明该算法较传统最小二乘支持向量回归机算法,其回归估计函数的预测能力明显提高,具有一定的实用价值。

Least Squares Support Vector Machine Based on Exponentially Weighted Feature

According to the basic principle of support vector regression algorithm,for the difference of features correlative degree to the regression,the affect of parameters to the performance of forecast and taking into account the significance of weighted feature after normalization,least squares support vector regression machine（LS-SVR） based on weighted feature is proposed in this paper,in which,least squares support vector regression algorithm is used to reduce the number of parameters and exponentially weighted feature is used to improve prediction accuracy,the weighting coefficients are determined by the grey correlation degree approach.In the meantime,the effectiveness of the algorithm is demonstrated in forecasting the actual stock price.The experimental results show that it is superior to classical support vector machine and can significantly improve the predictive ability.