| 基于KPCA的决策树方法及其应用 |
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| 引用本文： | 饶秀琪,张国基.基于KPCA的决策树方法及其应用[J].计算机工程与设计,2007,28(7):1612-1613. |
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| 作者姓名： | 饶秀琪 张国基 |
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| 作者单位： | 华南理工大学,数学科学学院,广东,广州,510640 |
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| 摘 要： | 主成分分析(PCA)作为一种数据减少技术常用于构造决策树,有利于降低树的复杂度和提高分类精度,但在处理非线性问题时往往不能取得好的效果.针对上述情况,提出了一种基于核主成分分析(KPCA)的决策树方法.实验结果表明,该方法是可行的和有效的,且在分类精度、方差贡献率等方面优于基于PCA的决策树.
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| 关 键 词： | 决策树 主成分分析 核主成分分析 数据减少技术 客户流失分析 KPCA 决策树方法 应用 application based method tree 方差贡献率 结果 实验 核主成分分析 情况 效果 线性问题 处理 分类精度 复杂度 构造 数据 |
| 文章编号： | 1000-7024（2007）07-1612-02 |
| 修稿时间： | 2006-03-05 |
Decision tree method based on KPCA and its application |
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| RAO Xiu-qi,ZHANG Guo-ji.Decision tree method based on KPCA and its application[J].Computer Engineering and Design,2007,28(7):1612-1613. |
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| Authors: | RAO Xiu-qi ZHANG Guo-ji |
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| Affiliation: | School of Mathematical Sciences, South China University of Technology, Guangzhou 510640, China |
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| Abstract: | Principal component analysis (PCA) is a popular data reduction technique for building decision tree. The complexity can be reduced and the classification precision of decision tree is improved. But it has drawbacks when it is used to solve nonlinear problem. Aimed at the foregoing point,a method of decision tree based on kernel principal component analysis (KPCA) is proposed. The ex-perimental result shows that it is feasible and effective. In comparison with PCA decision,there is also superior performance in KPCA decision tree. |
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| Keywords: | decision tree PCA KPCA data reduction technique customer chum analysis |
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