A spatial heterogeneity-based rough set extension for spatial data
文献类型：期刊论文
| 作者 | Bai, Hexiang1; Li, Deyu1,2; Ge, Yong3 ; Wang, Jinfeng3
|
| 刊名 | INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
 |
| 出版日期 | 2019-02-01 |
| 卷号 | 33期号:2页码:240-268 |
| 关键词 | Rough set model spatial information table spatial heterogeneity spatial autocorrelation feature selection |
| ISSN号 | 1365-8816 |
| DOI | 10.1080/13658816.2018.1524148 |
| 通讯作者 | Bai, Hexiang(baihx@sxu.edu.cn) |
| 英文摘要 | When classical rough set (CRS) theory is used to analyze spatial data, there is an underlying assumption that objects in the universe are completely randomly distributed over space. However, this assumption conflicts with the actual situation of spatial data. Generally, spatial heterogeneity and spatial autocorrelation are two important characteristics of spatial data. These two characteristics are important information sources for improving the modeling accuracy of spatial data. This paper extends CRS theory by introducing spatial heterogeneity and spatial autocorrelation. This new extension adds spatial adjacency information into the information table. Many fundamental concepts in CRS theory, such as the indiscernibility relation, equivalent classes, and lower and upper approximations, are improved by adding spatial adjacency information into these concepts. Based on these fundamental concepts, a new reduct and an improved rule matching method are proposed. The new reduct incorporates spatial heterogeneity in selecting the feature subset which can preserve the local discriminant power of all features, and the new rule matching method uses spatial autocorrelation to improve the classification ability of rough set-based classifiers. Experimental results show that the proposed extension significantly increased classification or segmentation accuracy, and the spatial reduct required much less time than classical reduct. |
| WOS关键词 | ATTRIBUTE REDUCTION ; LOCAL INDICATORS ; RULE ACQUISITION ; CLASSIFICATION ; SEGMENTATION ; ASSOCIATION ; FUZZY ; EXTRACTION ; SELECTION ; DEFECTS |
| WOS研究方向 | Computer Science ; Geography ; Physical Geography ; Information Science & Library Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000450550600002 |
| 出版者 | TAYLOR & FRANCIS LTD |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/51490]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Bai, Hexiang |
| 作者单位 | 1.Shanxi Univ, Sch Comp & Informat Technol, Taiyuan, Shanxi, Peoples R China 2.Shanxi Univ, Key Lab Computat Intelligence & Chinese Informat, Minist Educ, Taiyuan, Shanxi, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Bai, Hexiang,Li, Deyu,Ge, Yong,et al. A spatial heterogeneity-based rough set extension for spatial data[J]. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,2019,33(2):240-268. |
| APA | Bai, Hexiang,Li, Deyu,Ge, Yong,&Wang, Jinfeng.(2019).A spatial heterogeneity-based rough set extension for spatial data.INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,33(2),240-268. |
| MLA | Bai, Hexiang,et al."A spatial heterogeneity-based rough set extension for spatial data".INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 33.2(2019):240-268. |
入库方式： OAI收割
来源：地理科学与资源研究所
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