A fuzzy rough sets-based data-driven approach for quantifying local and overall fuzzy relations between variables for spatial data
文献类型：期刊论文
| 作者 | Bai, Hexiang1,2; Jing, Junhao1,2; Li, Deyu1,2; Ge, Yong3 |
| 刊名 | APPLIED SOFT COMPUTING
 |
| 出版日期 | 2024-09-01 |
| 卷号 | 162页码:15 |
| 关键词 | Fuzzy sets Fuzzy rough sets Spatial heterogeneity Geographical detector Geographically weighted regression |
| ISSN号 | 1568-4946 |
| DOI | 10.1016/j.asoc.2024.111848 |
| 英文摘要 | Exploring the relationships between variables is a crucial component in comprehending geographical phenomena. Most existing methods ignore the vagueness hidden in spatial data when quantifying this relation, which may lead to a partial or even wrong understanding of geographical phenomena as vagueness is an intrinsic property of them. This paper uses fuzzy rough sets for quantifying local and overall variable relationships to address this limitation, relying on the consistent degree between variables. This approach uses a sliding window to scan the entire study area and build a local region for each object. The local variable relation is quantified using the local average membership degree to the positive region for each object during the scan. The overall variable relation in the whole study area is quantified using the median value of the local consistent degree between variables in every local region, and the entropy of the normalized local consistent degree is used to measure the corresponding spatial heterogeneity. The proposed method can detect and compare local and overall variable relations. Comparison experiments on five publicly accessible datasets demonstrate the effectiveness of the proposed method and show that it can reveal patterns missed by geographically weighted regression and geographical detectors, as it models rather than ignores vagueness uncertainty. |
| WOS关键词 | GEOGRAPHICALLY WEIGHTED REGRESSION ; ATTRIBUTE REDUCTION ; EXPANSION METHOD ; APPROXIMATION ; MODELS |
| 资助项目 | Fundamental Research Program of Shanxi Province[202303021221073] ; National Natural Science Foundation of China[41871286] ; National Natural Science Foundation of China[62072294] ; National Natural Science Foundation of China[62072291] ; The 1331 Engineering Project of Shanxi Province, China |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:001257533900001 |
| 出版者 | ELSEVIER |
| 资助机构 | Fundamental Research Program of Shanxi Province ; National Natural Science Foundation of China ; The 1331 Engineering Project of Shanxi Province, China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/206202]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Bai, Hexiang |
| 作者单位 | 1.Shanxi Univ, Key Lab Computat Intelligence & Chinese Informat P, Minist Educ, Taiyuan 030006, Shanxi, Peoples R China 2.Shanxi Univ, Sch Comp & Informat Technol, Taiyuan 030006, Shanxi, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Bai, Hexiang,Jing, Junhao,Li, Deyu,et al. A fuzzy rough sets-based data-driven approach for quantifying local and overall fuzzy relations between variables for spatial data[J]. APPLIED SOFT COMPUTING,2024,162:15. |
| APA | Bai, Hexiang,Jing, Junhao,Li, Deyu,&Ge, Yong.(2024).A fuzzy rough sets-based data-driven approach for quantifying local and overall fuzzy relations between variables for spatial data.APPLIED SOFT COMPUTING,162,15. |
| MLA | Bai, Hexiang,et al."A fuzzy rough sets-based data-driven approach for quantifying local and overall fuzzy relations between variables for spatial data".APPLIED SOFT COMPUTING 162(2024):15. |
入库方式： OAI收割
来源：地理科学与资源研究所
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