Rule Generation based on Novel Kernel Intuitionistic Fuzzy Rough Set Model

ASIA unversity > 管理學院 > 經營管理學系 > 期刊論文 > Item 310904400/111487

jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://asiair.asia.edu.tw/ir/handle/310904400/111487

题名:	Rule Generation based on Novel Kernel Intuitionistic Fuzzy Rough Set Model
作者:	林國平;Lin, Kuo-Ping;*;Hung, K.-C.;Hung, K.-C.;Lin, C.-L.;Lin, C.-L.
贡献者:	經營管理學系
日期:	2018-02
上传时间:	2018-10-09 05:41:22 (UTC+0)
摘要:	This paper develops a novel kernel intuitionistic fuzzy rough set (KIFRS) model as a hybrid model to improve the effects of rule generation based on rough sets. The KIFRS model adopts new kernel intuitionistic fuzzy clustering (KIFCM) to enhance the performance of rough set theory (RST). To effectively improve the rule generation based on RST, the proposed hybrid method first adopts KIFCM to cluster raw data into similarity groups. Based on the KIFCM results, the RST can obtain superior performance in generating rules. Two benchmark machine learning data sets from the UCI machine learning repository are used to examine the performance of the developed model. The results show that the KIFRS model achieves superior performance to those of the traditional decision tree and rough set models.
關聯:	IEEE Access
显示于类别:	[經營管理學系 ] 期刊論文

文件中的档案:

档案	大小	格式	浏览次数
index.html	0Kb	HTML	364	检视/开启

在ASIAIR中所有的数据项都受到原著作权保护.