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| 一种基于粗糙间隔的模糊支持向量机 | |
| 引用本文： | 李凯,卢霄霞.一种基于粗糙间隔的模糊支持向量机[J].电子学报,2013,41(6):1183-1187. |
| 作者姓名： | 李凯 卢霄霞 |
| 作者单位： | 1. 河北大学数学与计算机学院, 河北 保定 071002; 2. 包头供电局电能计量中心, 内蒙古 包头 014030 |
| 基金项目： | 国家自然科学基金(No.61073121);河北省自然科学基金(No.F2012201014) |
| 摘 要： | 以模糊支持向量机(FSVM)为基础,同时考虑样本在间隔中的位置对决策超平面的影响,提出了基于粗糙间隔的模糊支持向量机(RFSVM).通过计算各个数据点的模糊隶属度,并利用最大化粗糙间隔方法,对具有隶属度的数据进行训练以获得决策超平面.在此算法中,位于下间隔中的训练点比边界域中的训练点具有较大的惩罚值,以便更好地减少噪声或野点对超平面的影响.利用选择的标准数据集对几种不同算法进行了实验比较,结果表明了RFSVM算法的有效性. |
| 关 键 词： | 模糊支持向量机 粗糙间隔 分类 正确率 |
| 收稿时间： | 2010-11-15 |
A Rough Margin Based Fuzzy Support Vector Machine |
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| LI Kai,LU Xiao-xia.A Rough Margin Based Fuzzy Support Vector Machine[J].Acta Electronica Sinica,2013,41(6):1183-1187. | |
| Authors: | LI Kai LU Xiao-xia |
| Affiliation: | 1. School of Mathematics and Computer, Hebei University, Baoding, Hebei 071002, China; 2. Power Measurement Center of Baotou Power Supply Bureau, Baotou, Inner Mongolia 014030, China |
| Abstract: | Based on fuzzy support vector machine(FSVM),we presented a rough margin based fuzzy support vector machine(RFSVM)by introducing the effects of positions of training samples in the margin on decision hyper-plane in this paper.After computing the degree of fuzzy membership of each training point,we used these data for training to obtain the decision hyper-plane by maximizing rough margin's method.In this algorithm,points in the lower margin have major penalty than those in the boundary.We compared RFSVM with other support vector machine algorithms on several benchmark datasets.Experimental results show that RFSVM is effective and feasible. |
| Keywords: | fuzzy support vector machine(FSVM) rough margin classification accuracy |
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