| 粗糙集的模糊性度量与SVM的混合分类算法 |
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| 引用本文： | 任小康,孙正兴,郝瑞芝. 粗糙集的模糊性度量与SVM的混合分类算法[J]. 计算机工程与应用, 2010, 46(7): 46-48. DOI: 10.3778/j.issn.1002-8331.2010.07.014 |
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| 作者姓名： | 任小康 孙正兴 郝瑞芝 |
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| 作者单位： | 西北师范大学数学与信息科学学院,兰州,730070 |
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| 摘 要： | 采用信息熵的方法来度量粗糙集的模糊性可以在约简之前对粗糙的决策属性进行预处理，从而消除因决策属性的冗余而带来的分类决策的偏差。结合 SVM在解决小样本、非线性及高维模式识别问题中表现出许多特有的优势。对该类别的方法进行了改进，并对分类的结果进行了测试。
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| 关 键 词： | 信息熵 粗糙集 模糊度 约简 支持向量机 |
| 收稿时间： | 2009-04-27 |
| 修稿时间： | 2009-7-2 |
Measure of rough sets's fuzziness and SVM hybrid classification algorithm |
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| REN Xiao-kang,SUN Zheng-xing,HAO Rui-zhi. Measure of rough sets's fuzziness and SVM hybrid classification algorithm[J]. Computer Engineering and Applications, 2010, 46(7): 46-48. DOI: 10.3778/j.issn.1002-8331.2010.07.014 |
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| Authors: | REN Xiao-kang SUN Zheng-xing HAO Rui-zhi |
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| Affiliation: | REN Xiao-kang,SUN Zheng-xing,HAO Rui-zhi College of Mathematics & Information Technology,Northwest Normal University,Lanzhou 730070,china |
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| Abstract: | This paper uses the information entropy method to measure the rough set's fuzziness,and makes the pretreatment before the reduction of rough's decision attribute with eliminating the difference which due to the redundance of decision attribute.Combination of SVM in solving the small sample,nonlinear and high dimensional pattern recognition problem has a lot of unique performance advantages.In this paper,an improved algorithm is given and the classification results are tested. |
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| Keywords: | information entropy rough set fuzzy degree reduction Support Vector Machine(SVM) |
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