Interval data driven construction of shadowed sets with application to linguistic word modelling
Li, Chengdong1; Yi, Jianqiang2; Wang, Hongkai3; Zhang, Guiqing1; Li, Junqing4
发表期刊INFORMATION SCIENCES
ISSN0020-0255
2020
卷号507页码:503-521
通讯作者Li, Chengdong(lichengdong@sdjzu.edu.cn)
摘要The interval data from different surveyed persons for one linguistic word can reflect the intra- and inter-uncertainties of the word. This study shows how to construct shadowed set models for linguistic words based on the surveyed interval data. Firstly, corresponding to the popularly used fuzzy sets for linguistic words, four kinds of shadowed sets are introduced according to their shapes and named as the normal, the left-shoulder, the right-shoulder, and the non-cored shadowed sets. A data-driven approach that utilizes different statistics to determine the shapes and parameters of the shadowed set models is then proposed. The proposed data-driven approach includes two methods; the first is the tolerance limit method depending on the mean and standard deviation of the remaining interval data after pre-processing, whilst the other is the percentile method relying on the percentiles of the remaining interval data. Additionally, to evaluate the modelling performance, three novel indices are presented to measure the uncertainty-capture capability and accuracy of the constructed shadowed set models. Finally, the proposed approach is applied to two real-world problems. One is the modelling of 32 words in a codebook, and the other is the modelling of the thermal feeling words. The proposed methods are compared with other interval data driven methods, e.g. the enhanced interval approach and the fuzzy statistic method. Our results show that the proposed percentile method performs better in both applications. The proposed approach can also be applied to some other linguistic word modelling applications when it is reasonable to adopt shadowed sets as the words' models. (C) 2018 Elsevier Inc. All rights reserved.
关键词Shadowed set Interval data Data driven Tolerance limit Percentile statistic
DOI10.1016/j.ins.2018.11.018
关键词[WOS]TYPE-2 FUZZY-SETS ; 3-WAY APPROXIMATIONS ; C-MEANS
收录类别SCI
语种英语
资助项目National Natural Science Foundation of China[61473176] ; National Natural Science Foundation of China[61105077] ; National Natural Science Foundation of China[61573225] ; Taishan Scholar Project of Shandong Province ; National Natural Science Foundation of China[61473176] ; National Natural Science Foundation of China[61105077] ; National Natural Science Foundation of China[61573225] ; Taishan Scholar Project of Shandong Province
项目资助者National Natural Science Foundation of China ; Taishan Scholar Project of Shandong Province
WOS研究方向Computer Science
WOS类目Computer Science, Information Systems
WOS记录号WOS:000489000500031
出版者ELSEVIER SCIENCE INC
引用统计
被引频次:47[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/26610
专题复杂系统认知与决策实验室_飞行器智能技术
通讯作者Li, Chengdong
作者单位1.Shandong Jianzhu Univ, Sch Informat & Elect Engn, Jinan 250101, Shandong, Peoples R China
2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
3.Univ Jinan, Sch Math Sci, Jinan 250022, Shandong, Peoples R China
4.Liaocheng Univ, Sch Comp Sci, Liaocheng 252059, Shandong, Peoples R China
推荐引用方式
GB/T 7714
Li, Chengdong,Yi, Jianqiang,Wang, Hongkai,et al. Interval data driven construction of shadowed sets with application to linguistic word modelling[J]. INFORMATION SCIENCES,2020,507:503-521.
APA Li, Chengdong,Yi, Jianqiang,Wang, Hongkai,Zhang, Guiqing,&Li, Junqing.(2020).Interval data driven construction of shadowed sets with application to linguistic word modelling.INFORMATION SCIENCES,507,503-521.
MLA Li, Chengdong,et al."Interval data driven construction of shadowed sets with application to linguistic word modelling".INFORMATION SCIENCES 507(2020):503-521.
条目包含的文件
条目无相关文件。
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Li, Chengdong]的文章
[Yi, Jianqiang]的文章
[Wang, Hongkai]的文章
百度学术
百度学术中相似的文章
[Li, Chengdong]的文章
[Yi, Jianqiang]的文章
[Wang, Hongkai]的文章
必应学术
必应学术中相似的文章
[Li, Chengdong]的文章
[Yi, Jianqiang]的文章
[Wang, Hongkai]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。