Fuzzy c-Regression Models for Fuzzy Numbers on a Graph

被引:0
|
作者
Higuchi, Tatsuya [1 ]
Miyamoto, Sadaaki [1 ]
Endo, Yasunori [1 ]
机构
[1] Univ Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 3058573, Japan
关键词
graph structure; clustering; c-regression; outlier detection; dimensionality reduction;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
With the assumption that the vertices have numerical values. The aim of this paper is to construct regression models to estimate the values from their relationship on the graph by defining the vertex and the numerical value as an independent variable and a dependent variable, respectively. Given the condition that near vertices have close values, k-Nearest Neighbor regression models (KNN) has been proposed. However, the condition is not satisfied when some near vertices have different values. To overcome such difficulty, c-regression which classify data points into some clusters has been proposed to improve performance of regression analysis. We moreover propose new c-regression models on a graph with fuzzy numbers on vertices and show some numerical examples.
引用
收藏
页码:521 / 534
页数:14
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