[1]李蒙蒙,尚志刚,李志辉.结合投影与近邻操作的支持向量快速筛选方法[J].郑州大学学报(工学版),2017,38(03):49-53.[doi:10.13705/j.issn.1671-6833.2016.06.003]
　Li Mengmeng,Shang Zhigang,Li Zhihui.Fast Method to Filter Support Vectors Combined with Operation of Projection and Nearest Neighbors’ Selection[J].Journal of Zhengzhou University (Engineering Science),2017,38(03):49-53.[doi:10.13705/j.issn.1671-6833.2016.06.003]
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结合投影与近邻操作的支持向量快速筛选方法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
38
期数:
2017年03期
页码:
49-53
栏目:
出版日期:
2017-05-28

文章信息/Info

Title:
Fast Method to Filter Support Vectors Combined with Operation of Projection and Nearest Neighbors’ Selection
作者:
李蒙蒙; 尚志刚; 李志辉
郑州大学电气工程学院,河南郑州,450001
Author(s):
Li Mengmeng; Shang Zhigang; Li Zhihui
School of Electrical Engineering, Zhengzhou University, Zhengzhou, Henan 450001
关键词:
支持向量机; 支持向量; Fisher投影; k-近邻; 快速筛选
Keywords:
DOI:
10.13705/j.issn.1671-6833.2016.06.003
文献标志码:
A
摘要:
为减少支持向量机(SVM)的计算负担,提高运算效率,并保证分类精度,提出一种结合投影与近邻操作的支持向量快速筛选方法.该方法利用Fisher投影轴的全局特性将其作为SVM最优分类面的近似法方向,在该方向快速筛除大量非支持向量,将分类边界附近的样本集作为备选支持向量集,同时为解决投影操作未考虑样本局部结构信息造成的误删支持向量的问题,结合近邻操作回选样本空间中备选支持向量的近邻样本更新扩充备选支持向量集,以该子集中的样本作为SVM的输入.在多个UCI标准数据集上的实验结果表明,该方法在充分保证分类精度的前提下有效降低了SVM的计算负担,具有较好的推广性.
Abstract:
To reduce computational burden and improve operation efficiency of support vector machine (SVM) while ensuring classification accuracy,a fast method to filter support vectors combined with operation of projection and nearest neighbors’ selection was proposed.Considering the global characteristics of Fisher projection,it could be viewed as the approximate normal directions of SVM optimal hyperplane and filtered out a large number of non-support-vectors in this direction.The samples near the classification obtained boundary were regarded as alternative support vectors set.Neighborhood operation was combined to solve the problem that some support vectors might be filtered out mistakenly regardless of the local structure information.A number of nearest neighbors of the alternative support vectors were selected backward from the samples space to update and expand the alternative support vectors set.The sets was treated as the SVM input.The experimental results on several UCI standard data sets showed that the fast method had good generalization performance and reduced the computational burden effectively under the premise of fully guaranteed classification accuracy.

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