IGSNRR OpenIR
A spatial heterogeneity-based rough set extension for spatial data
Bai, Hexiang1; Li, Deyu1,2; Ge, Yong3; Wang, Jinfeng3
2019-02-01
Source PublicationINTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
ISSN1365-8816
Volume33Issue:2Pages:240-268
Corresponding AuthorBai, Hexiang(baihx@sxu.edu.cn)
AbstractWhen 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.
KeywordRough set model spatial information table spatial heterogeneity spatial autocorrelation feature selection
DOI10.1080/13658816.2018.1524148
WOS KeywordATTRIBUTE REDUCTION ; LOCAL INDICATORS ; RULE ACQUISITION ; CLASSIFICATION ; SEGMENTATION ; ASSOCIATION ; FUZZY ; EXTRACTION ; SELECTION ; DEFECTS
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Geography ; Physical Geography ; Information Science & Library Science
WOS SubjectComputer Science, Information Systems ; Geography ; Geography, Physical ; Information Science & Library Science
WOS IDWOS:000450550600002
PublisherTAYLOR & FRANCIS LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/51491
Collection中国科学院地理科学与资源研究所
Corresponding AuthorBai, Hexiang
Affiliation1.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
Recommended Citation
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.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Bai, Hexiang]'s Articles
[Li, Deyu]'s Articles
[Ge, Yong]'s Articles
Baidu academic
Similar articles in Baidu academic
[Bai, Hexiang]'s Articles
[Li, Deyu]'s Articles
[Ge, Yong]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Bai, Hexiang]'s Articles
[Li, Deyu]'s Articles
[Ge, Yong]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.