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Incorporating spatial association into statistical classifiers: local pattern-based prior tuning

文献类型：期刊论文

作者Bai, Hexiang1; Cao, Feng1; Atkinson, M. Peter3; Chen, Qian1; Wang, Jinfeng2; Ge, Yong2
刊名INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
出版日期2020-03-12
页码38
关键词Spatial pattern statistical classifier spatial auto-logistic regression spatial data
ISSN号1365-8816
DOI10.1080/13658816.2020.1737702
通讯作者Ge, Yong(gey@lreis.ac.cn)
英文摘要This paper proposes a new classification method for spatial data by adjusting prior class probabilities according to local spatial patterns. First, the proposed method uses a classical statistical classifier to model training data. Second, the prior class probabilities are estimated according to the local spatial pattern and the classifier for each unseen object is adapted using the estimated prior probability. Finally, each unseen object is classified using its adapted classifier. Because the new method can be coupled with both generative and discriminant statistical classifiers, it performs generally more accurately than other methods for a variety of different spatial datasets. Experimental results show that this method has a lower prediction error than statistical classifiers that take no spatial information into account. Moreover, in the experiments, the new method also outperforms spatial auto-logistic regression and Markov random field-based methods when an appropriate estimate of local prior class distribution is used.
WOS关键词REMOTELY-SENSED IMAGES ; K-NN CLASSIFIER ; WEIGHTED REGRESSION ; COVER CHANGE ; EXTRACTION ; MODEL ; IDENTIFICATION ; INFORMATION ; PREDICTION ; EXPANSION
资助项目Strategic Priority Research Program of the Chinese Academy of Science[XDA19040501] ; Chinese National Science Fundation[41725006] ; National Natural Science Foundation of China[41871286] ; National Natural Science Foundation of China[61672331] ; Natural Science Foundation of Shanxi Province, China[201701D121055]
WOS研究方向Computer Science ; Geography ; Physical Geography ; Information Science & Library Science
语种英语
WOS记录号WOS:000519399200001
出版者TAYLOR & FRANCIS LTD
资助机构Strategic Priority Research Program of the Chinese Academy of Science ; Chinese National Science Fundation ; National Natural Science Foundation of China ; Natural Science Foundation of Shanxi Province, China
源URL[http://ir.igsnrr.ac.cn/handle/311030/133016]  
专题中国科学院地理科学与资源研究所
通讯作者Ge, Yong
作者单位1.Shanxi Univ, Sch Comp & Informat Technol, Taiyuan, Shanxi, Peoples R China
2.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China
3.Univ Lancaster, Engn Bldg, Lancaster, England
推荐引用方式
GB/T 7714		 Bai, Hexiang,Cao, Feng,Atkinson, M. Peter,et al. Incorporating spatial association into statistical classifiers: local pattern-based prior tuning[J]. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,2020:38.
APA Bai, Hexiang,Cao, Feng,Atkinson, M. Peter,Chen, Qian,Wang, Jinfeng,&Ge, Yong.(2020).Incorporating spatial association into statistical classifiers: local pattern-based prior tuning.INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,38.
MLA Bai, Hexiang,et al."Incorporating spatial association into statistical classifiers: local pattern-based prior tuning".INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE (2020):38.

入库方式： OAI收割

来源：地理科学与资源研究所

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