密度加权孪生支持向量回归机  

Density-weighted twin support vector regression

作　　者：程昊翔[1] 王坚[2] 

机构地区：[1]同济大学电子与信息工程学院,上海201804 [2]同济大学CIMS研究中心,上海201804

出　　处：《控制与决策》2016年第4期755-758,共4页Control and Decision

基　　金：国家自然科学基金面上项目(71273188);国家自然科学基金重大项目(91024031)

摘　　要：为了使数据集的内在分布更好地影响训练模型,提出一种密度加权孪生支持向量回归机算法.该算法通过k近邻算法计算获得每个数据点基于数据密度分布的密度加权值,并将密度加权值引入到标准孪生支持向量回归机算法中.算法能够很好地反映训练数据集的内在分布,使数据点准确影响训练模型.通过6个UCI数据集上的实验结果分析验证了所提出算法的有效性.To better impact the training model with the inherent distribution of the training dataset, a twin support vector regression called density-weighted twin support vector regression is proposed. Firstly, the density-weighted value is computed based on the -nearest neighbor algorithm. Then, the values of density-weighted are introduced to the standard twin support vector regression. It is found that the proposed algorithm can well reflect the inherent distribution of the training dataset and lead to a more accurate impact on the training model. Experimental results on six UCI datasets show the effectiveness of the proposed algorithm.

关 键 词：密度加权 K近邻法 内在分布 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]