计算机应用与软件Issue(5)：172-176,5.DOI:10.3969/j.issn.1000-386x.2014.05.044
基于层次K-均值聚类的支持向量机模型
A SUPPORT VECTOR MACHINE MODEL BASED ON HIERARCHICAL K-MEANS CLUSTERING
王秀华 1秦振吉1
作者信息
- 1. 晋中学院计算机学院 山西 晋中030600
- 折叠
摘要
Abstract
This paper presents an improved SVM learning model,it is based on hierarchical k-means clustering and is called as hierarchical k-means SVM (HKSVM),to solve the problem of SVM in low classification efficiency.The method first makes k-means clustering on every sample class respectively and calculates the centre of each class as well as trains SVM to get initial classifier;then it divides the clustering result into active class set and static class set according to the relationship between the hyperplane and the clustering result,and conducts deeper clustering on the active sets near to the hyperplane for obtaining even smaller classes,and calculates at the same time the class centres to train new SVM model,and corrects the classified hyperplane.This process is on ad infinitum until the more precise classifier is obtained.Adopting hierarchical k-means clustering-based SVM model and by incessant deep clustering on active class sets,more sample points are obtained near the classified hyperplane;however,in where farther from the hyperplane,the extracted training samples are not much so as to effectively compress the size of the training set,and significantly improve SVM’s learning efficiency while keeping its training precision.The experimental results on UCI benchmark datasets demonstrate that the proposed HKSVM model achieves higher classification efficiency and testing accuracy simultaneously on large-scale dataset.关键词
层次K-均值聚类/支持向量机/HKSVM模型/活动类集/静止类集Key words
Hierarchical k-means clustering/Support vector machine(SVM)/HKSVM model/Active class set/Static class set分类
信息技术与安全科学引用本文复制引用
王秀华,秦振吉..基于层次K-均值聚类的支持向量机模型[J].计算机应用与软件,2014,(5):172-176,5.
