A case-based reasoning strategy of integrating case-level and covariate-level reasoning to automatically select covariates for spatial prediction
| Autor: | Yi-Jie Wang, Cheng-Zhi Qin, Peng Liang, Liang-Jun Zhu, Zi-Yue Chen, Cheng-Long Wu, A-Xing Zhu |
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| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Annals of GIS, Vol 30, Iss 2, Pp 199-214 (2024) |
| Druh dokumentu: | article |
| ISSN: | 19475683 1947-5691 1947-5683 |
| DOI: | 10.1080/19475683.2024.2324398 |
| Popis: | ABSTRACTSpatial prediction is essential for obtaining the spatial distribution of geographic variables and selecting appropriate covariates for this process can be challenging, especially for non-expert users. For easing the burden of selecting the appropriate covariates, two case-based reasoning strategies, namely the most-similar-case and covariate-classification strategies, have been proposed for automated covariate selection. The former may suggest nonessential covariates due to its case-level reasoning way. And the latter with covariate-level reasoning may overlook related covariates and recommend fewer covariates than the case-level reasoning. In this study, we propose a new strategy of integrating case-level and covariate-level reasoning to effectively leverage the strengths of both previous strategies while also addressing their limitations. The proposed strategy is validated through a case study of automatically selecting covariates for digital soil mapping under reasoning with a case base containing 189 cases. The leave-one-out evaluation demonstrated that our proposed strategy outperformed the previous two strategies. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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