改进的SVM决策树分类算法  

An Improved Algorithm for SVM Decision Tree

作　　者：史朝辉[1] 王晓丹[1] 赵士敏[1] 杨建勋[1] 

机构地区：[1]空军工程大学导弹学院,陕西三原713800

出　　处：《空军工程大学学报（自然科学版）》2006年第2期32-35,共4页Journal of Air Force Engineering University(Natural Science Edition)

基　　金：陕西省自然科学基金资助项目(2004F36)

摘　　要：为解决多类分类问题,在分析SVM决策树分类器及存在问题的基础上,通过引入类间可分离性测度,并将其扩展到核空间,提出一种改进的SVM决策树分类器。实验表明了该分类算法对提高分类正确率的有效性。For the multi -class classification with Support Vector Machines （SVMs）, a decision tree architecture has been proposed for computational efficiency. But by SVM decision tree, the generalization ability depends on the tree structure. In this paper, to improve the generalization ability of SVM decision tree, a novel separability measure is defined based on the distribution of the training samples in the kernel space, and an improved SVM decision tree is provided. The theoretical analysis and experimental results show that this algorithm has higher generalization ability.

关 键 词：支持向量机 SVM决策树 可分离性测度 核空间 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]