粗糙集的模糊性度量与SVM的混合分类算法  

Measure of rough sets's fuzziness and SVM hybrid classification algorithm

作　　者：任小康[1] 孙正兴[1] 郝瑞芝[1] 

机构地区：[1]西北师范大学数学与信息科学学院,兰州730070

出　　处：《计算机工程与应用》2010年第7期46-48,共3页Computer Engineering and Applications

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。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.

关 键 词：信息熵 粗糙集 模糊度 约简 支持向量机 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]