The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Kuhn, M., & Johnson, K. (2013) · doi:10.1007/978-1-4614-6849-3 · Cited by 6,788
Cited by
Predicting into unknown space? Estimating the area of applicability of spatial prediction models
Meyer, H., & Pebesma, E. (2021) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13650 · Cited by 662
Self-citationMeyer, H., Reudenbach, C., Wöllauer, S., & Nauss, T. (2019) · Ecological Modelling · doi:10.1016/j.ecolmodel.2019.108815 · Cited by 509
Self-citationAssessing and improving the transferability of current global spatial prediction models
Ludwig, M., Moreno‐Martinez, A., Hölzel, N., Pebesma, E., & Meyer, H. (2023) · Global Ecology and Biogeography · doi:10.1111/geb.13635 · Cited by 92
Self-citationSpatial machine-learning model diagnostics: a model-agnostic distance-based approach
Brenning, A. (2023) · International Journal of Geographical Information Science · doi:10.1080/13658816.2022.2131789 · Cited by 33
Ismail, R. B. Y., Bozorg-Omid, F., Osei, J. H. N., Pi-Bansa, S., Frempong, K. K., Ofei, M. K., Boakye, H. A., Ansah-Owusu, J., Akorful, S.-C. A., Tawiah-Mensah, C. N. L., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-51780-7 · Cited by 11
“With goal-oriented validation strategies for spatio-temporal prediction models, the neglected problem of dependencies caused by the nature of spatio-temporal data can be addresse 48 .”
Background
Metadata
- Authors
- Meyer, H., Reudenbach, C., Hengl, T., Katurji, M., & Nauss, T.
- Venue
- Environmental Modelling & Software
- Publisher
- Elsevier BV
- Published
- 2018-03
- Volume
- 101
- Pages
- 1-9
- ISSN
- 1364-8152 (ISSN-L)
- OpenAlex
- W2773188111
- Cited by
- 565
- References
- 2
- FWCI
- 8.43
- Type
- journal article
- Language
- en
- Open access
- green
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- The paper
- Meyer, H., Reudenbach, C., Hengl, T., Katurji, M., & Nauss, T. (2018). Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation. Environmental Modelling & Software. https://doi.org/10.1016/j.envsoft.2017.12.001
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
