A Constructing Method of Fuzzy Classifier Using Kernel K-means Clustering Algorithm

被引:0
|
作者
Yang, Aimin [1 ]
Li, Qing [2 ]
Li, Xinguang [1 ]
机构
[1] Guangdong Univ Foreign Studies, Sch Informat, Guangzhou, Guangdong, Peoples R China
[2] Guangdong Univ Foreign Studies, Sch Business, Guangzhou, Guangdong, Peoples R China
关键词
fuzzy classifier; kernel k-means clustering; triangle membership function; genetic algorithms; fuzzy rule; FEATURE SPACE;
D O I
10.1109/KAM.2009.5
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
A constructing method of fuzzy classifier using kernel k-means clustering algorithm is instroduced in this paper. This constructing method are divided into three phases,namely clustering phase,fuzzy rule created phanse and parameters modified phase. firstly, the original sample space is mapped into a high dimensional feature space by selecting appropriate kernel function. In the feature space, training samples are grouped into some clusters by kernel k-means clustering algorithm. Then for each created cluster, a fuzzy rule is defined whith the appropriate membership function. Finally, Some parameters of fuzzy classifier are chosen by GAs. The experiment results show the proposed fuzzy classifer has very high classification accuracy by the the comparision results with the similar approach,and has the better applied values.
引用
收藏
页码:73 / +
页数:2
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