A rough margin based support vector machine
A rough margin based support vector machine
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DOI：
10.1016/j.ins.2007.12.012
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发表时间：
2008-05
期刊：
影响因子：
--
通讯作者：
Junhua Zhang;Yuanyuan Wang
中科院分区：
文献类型：
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作者：
Junhua Zhang;Yuanyuan Wang
将粗糙集理论引入到支持向量机中，提出了一种基于粗糙间隔的支持向量机（RMSVM）来处理离群点引起的过拟合问题。与经典的SVM相似，RMSVM搜索最大化由上下边界定义的粗糙边界的分离超平面。通过这种方式，更多的数据点被自适应地考虑，而不是在经典的SVM中使用的几个极值点。此外，不同的支持向量可能会有不同的效果的学习的分离超平面取决于它们在粗糙边缘的位置。下边缘中的点比粗糙边缘的边界中的点具有更大的影响。在6个基准数据集上的实验结果表明，与经典的ν-SVM相比，该算法在不增加计算量的情况下，分类精度得到了提高。
By introducing the rough set theory into the support vector machine (SVM), a rough margin based SVM (RMSVM) is proposed to deal with the overfitting problem due to outliers. Similar to the classical SVM, the RMSVM searches for the separating hyper-plane that maximizes the rough margin, defined by the lower and upper margin. In this way, more data points are adaptively considered rather than the few extreme value points used in the classical SVM. In addition, different support vectors may have different effects on the learning of the separating hyper-plane depending on their positions in the rough margin. Points in the lower margin have more effects than those in the boundary of the rough margin. From experimental results on six benchmark datasets, the classification accuracy of this algorithm is improved without additional computational expense compared with the classical ν-SVM.