摘要
标准近似支持向量机受类别差异影响和噪声、野值数据干扰较重,使得分类能力不高.提出一种改进的近似支持向量机算法——加权近似支持向量机,通过为不同类别设定不同的惩罚参数和为每个样本引入模糊隶属值,有效补偿类别差异带来的倾向性并去除噪声和野值数据的影响.模糊隶属函数的选取采用样本与类中心的距离和样本紧密度的加权平均值计算,以有效去除噪声和野值数据的干扰.经过分析,改进后的算法可近似归结为一种岭回归模型.实验表明,与标准近似支持向量机相比,该算法有更好的分类能力.
Since proximal support vector machine （PSVM） is susceptible to uneven class sizes and is sensitive to outliers and noises in the training set, a robust PSVM was proposed. By imposing fuzzy memberships to each data point and introducing different error penalties for different classes, the robustness of PSVM was greatly enhanced. Both the affinity among samples and the relation between a sample and its class center were considered when calculating fuzzy memberships. Moreover, the similarity between the algorithm and ridge regression model was well demonstrated. Experiment results show that the robust PSVM has demonstrated enhanced classification ability.
出处
《北京航空航天大学学报》
EI
CAS
CSCD
北大核心
2007年第9期1090-1093,共4页
Journal of Beijing University of Aeronautics and Astronautics
关键词
分类
支持向量机
类别差异
模糊隶属值
classification
support vector machine
uneven class sizes
fuzzy memberships
