基于邻域粗糙集的支持向量机分类方法研究  

Research of Support Vector Classifier Based on Neighborhood Rough Set

作　　者：韩虎[1,2] 党建武[2] 任恩恩[2] 

机构地区：[1]兰州交通大学数理与软件学院,兰州730070 [2]兰州交通大学电子与信息工程学院,兰州730070

出　　处：《计算机科学》2010年第2期229-231,285,共4页Computer Science

基　　金：甘肃省自然科学基金(2008GS02625);甘肃省教育厅科研基金(0804-01)资助

摘　　要：针对支持向量机方法对高维大规模数据无法直接处理和对异常样本敏感的问题,提出了一种基于邻域粗糙集模型的改进支持向量机。该算法从两个方面对训练样本集进行预处理:一方面利用邻域粗糙集模型中对象邻域的上、下近似,寻找两种类别的交界部分,从而减小问题规模;然后通过对交界部分样本进行混淆度分析,剔除那些混杂在另一类样本中的异常样本或噪声数据。另一方面利用属性重要性度量对样本集进行属性约简与属性加权处理。基于合成数据集与标准数据集的有关实验证实了该算法的有效性。Support vector machine can not directly deal with high dimension and large scale training set and it is sensi- tive to abnormal samples, an improved support vector classifier based on neighborhood rough set was proposed. In the paper,data preprocessing was done on training set from two different sides. On the one hand, neighborhood rough set was used to find these samples in boundary and obtain a reduced training set,at the same time, those abnormal samples which not only lead to over-learning but also decrease the generalization ability were deleted. On the other hand, attri- bute reduction was done and feature weight was imported based on attribute significance because different feature effects differently on classification. At last several comparative experiments using synthetic and real life data set show the performance and the effectivity of the method

关 键 词：支持向量机 邻域粗糙集 预处理 属性约简 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术] V328[自动化与计算机技术—计算机科学与技术]