| 改进的SVM决策树分类算法 |
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| 引用本文： | 史朝辉,王晓丹,赵士敏,杨建勋.改进的SVM决策树分类算法[J].空军工程大学学报,2006,7(2):32-35. |
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| 作者姓名： | 史朝辉 王晓丹 赵士敏 杨建勋 |
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| 作者单位： | 空军工程大学导弹学院 陕西三原713800 |
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| 基金项目： | 陕西省自然科学基金资助项目(2004F36) |
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| 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。
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| 关 键 词： | 支持向量机 SVM决策树 可分离性测度 核空间 |
| 文章编号： | 1009-3516（2006）02-0032-04 |
| 收稿时间： | 2005-04-13 |
| 修稿时间： | 2005年4月13日 |
An Improved Algorithm for SVM Decision Tree |
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| SHI Zhao-hui,WANG Xiao-dan,ZHAO Shi-min,YANG Jian-xun.An Improved Algorithm for SVM Decision Tree[J].Journal of Air Force Engineering University(Natural Science Edition),2006,7(2):32-35. |
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| Authors: | SHI Zhao-hui WANG Xiao-dan ZHAO Shi-min YANG Jian-xun |
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| Institution: | The Missile Institute, Air Force Engineering University, Sanyuan, Shaanxi 713800, China |
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| Abstract: | 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. |
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| Keywords: | support vector machine SVM decision tree separability measure the kernel space |
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