Fuzzy c-Regression Models with Cluster Characteristics Clarification

被引：0
|
作者：
Nasada, Shinpei [1 ]
Honda, Katsuhiro [1 ]
Ubukata, Seiki [1 ]
Notsu, Akira [2 ]
机构：
[1] Osaka Prefecture Univ, Grad Sch Engn, Sakai, Osaka, Japan
[2] Osaka Prefecture Univ, Grad Sch Humanities & Sustainable Syst Sci, Sakai, Osaka, Japan
来源：
2019 INTERNATIONAL CONFERENCE ON FUZZY THEORY AND ITS APPLICATIONS (IFUZZY) | 2019年
关键词：
fuzzy clustering; Regression; variable selection;
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：
In order to improve the comparative interpretability among cluster-wise local regression models, this paper proposes a modified fuzzy c-regression models (FCRM), which is a fuzzy c-means (FCM)-type switching regression. Based on the combined concepts of ridge regression and intra-cluster exclusive variable selection, cluster-wise meaningful explanatory variables are emphasized. Additionally, it is further extended with the LASSO concept for reducing the inappropriate influences of larger coefficient values. The characteristics of the proposed methods are demonstrated through some numerical experiments.
引用
收藏
页码：5 / 8
页数：4
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