| 支持向量机分类算法中多元变量共线性问题的改进 |
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| 引用本文： | 惠守博,王文杰.支持向量机分类算法中多元变量共线性问题的改进[J].计算机工程与设计,2006,27(8):1385-1388. |
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| 作者姓名： | 惠守博 王文杰 |
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| 作者单位： | 中国科学院,研究生院,北京,100039 |
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| 摘 要： | 结合核主成分分析的主因子提取和支持向量机的分类机理,提出了一种组合建模算法.应用核主成分分析过程作为预处理器,可以把共线性的多元变量糅合为几个主因子,但基本不损失有效信息.然后进行基于支持向量机的分类建模和预测.这样不仅可以防止共线性多元变量对模型的负面影响,还可以降低数据维数,减少支持向量机分类过程中的复杂度和运算量.最后用实验进行评估所得到的训练模型,实例说明了所提方法的有效性.
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| 关 键 词： | 核主成分分析 支持向量机算法 多元共线性 核函数 分类算法 机器学习 |
| 文章编号： | 1000-7024（2006）08-1385-04 |
| 收稿时间： | 2005-10-05 |
| 修稿时间： | 2005-10-05 |
Improvement of multi-variable's redundant attributes in classification algorithm of support vector machines |
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| HUI Shou-bo,WANG Wen-jie.Improvement of multi-variable''''s redundant attributes in classification algorithm of support vector machines[J].Computer Engineering and Design,2006,27(8):1385-1388. |
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| Authors: | HUI Shou-bo WANG Wen-jie |
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| Affiliation: | Graduate University, Chinese Academy of Sciences, Beijing 100039, China |
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| Abstract: | A novel hybrid algorithm based on principal components and classification principles of support vector machines is presented.The principal components analysis is applied as preprocessor so that the redundant attributes are deleted,and the efficient information is remain.Then,classification modeling and forecasting test based on SVM are put in practice.By this method,the negative influence of multi-variable's redundant attributes is greatly reduced,and the complexity in the process of SVM classification is highly decreased.The occupied memory is cut down.Finally,the experimental result show the effectiveness of the suggested hybrid method. |
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| Keywords: | kernel principal components analysis support vector machine redundant attributes kernel function classification algorithm machine learning |
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