FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2011279072> ?p ?o ?g. }

• W2011279072 endingPage "214" @default.
• W2011279072 startingPage "213" @default.
• W2011279072 abstract "Maps that categorise the landscape into discrete units are a cornerstone of many scientific, management and conservation activities. The accuracy of these maps is often the primary piece of information used to make decisions about the mapping process or judge the quality of the final map. Variance is critical information when considering map accuracy, yet commonly reported accuracy metrics often do not provide that information. Various resampling frameworks have been proposed and shown to reconcile this issue, but have had limited uptake. In this paper, we compare the traditional approach of a single split of data into a training set (for classification) and test set (for accuracy assessment), to a resampling framework where the classification and accuracy assessment are repeated many times. Using a relatively simple vegetation mapping example and two common classifiers (maximum likelihood and random forest), we compare variance in mapped area estimates and accuracy assessment metrics (overall accuracy, kappa, user, producer, entropy, purity, quantity/allocation disagreement). Input field data points were repeatedly split into training and test sets via bootstrapping, Monte Carlo cross-validation (67:33 and 80:20 split ratios) and k-fold (5-fold) cross-validation. Additionally, within the cross-validation, we tested four designs: simple random, block hold-out, stratification by class, and stratification by both class and space. A classification was performed for every split of every methodological combination (100’s iterations each), creating sampling distributions for the mapped area of each class and the accuracy metrics. We found that regardless of resampling design, a single split of data into training and test sets results in a large variance in estimates of accuracy and mapped area. In the worst case, overall accuracy varied between ~40–80% in one resampling design, due only to random variation in partitioning into training and test sets. On the other hand, we found that all resampling procedures provided accurate estimates of error, and that they can also provide confidence intervals that are informative about the performance and uncertainty of the classifier. Importantly, we show that these confidence intervals commonly encompassed the magnitudes of increase or decrease in accuracy that are often cited in literature as justification for methodological or sampling design choices. We also show how a resampling approach enables generation of spatially continuous maps of classification uncertainty. Based on our results, we make recommendations about which resampling design to use and how it could be implemented. We also provide a fully worked mapping example, which includes traditional inference of uncertainty from the error matrix and provides examples for presenting the final map and its accuracy." @default.
• W2011279072 created "2016-06-24" @default.
• W2011279072 creator A5006251561 @default.
• W2011279072 date "2004-01-01" @default.
• W2011279072 modified "2023-09-25" @default.
• W2011279072 title "The physical geography of Northern EurasiaMaria Shahgedanova (Ed.); Oxford University Press, Oxford, 2003, 571pp., price £80.00, ISBN 0-19-823384-1" @default.
• W2011279072 doi "https://doi.org/10.1016/s0277-3791(03)00216-6" @default.
• W2011279072 hasPublicationYear "2004" @default.
• W2011279072 type Work @default.
• W2011279072 sameAs 2011279072 @default.
• W2011279072 citedByCount "0" @default.
• W2011279072 crossrefType "journal-article" @default.
• W2011279072 hasAuthorship W2011279072A5006251561 @default.
• W2011279072 hasConcept C105795698 @default.
• W2011279072 hasConcept C11413529 @default.
• W2011279072 hasConcept C119857082 @default.
• W2011279072 hasConcept C124101348 @default.
• W2011279072 hasConcept C149782125 @default.
• W2011279072 hasConcept C150921843 @default.
• W2011279072 hasConcept C154945302 @default.
• W2011279072 hasConcept C169258074 @default.
• W2011279072 hasConcept C19499675 @default.
• W2011279072 hasConcept C207609745 @default.
• W2011279072 hasConcept C33923547 @default.
• W2011279072 hasConcept C41008148 @default.
• W2011279072 hasConceptScore W2011279072C105795698 @default.
• W2011279072 hasConceptScore W2011279072C11413529 @default.
• W2011279072 hasConceptScore W2011279072C119857082 @default.
• W2011279072 hasConceptScore W2011279072C124101348 @default.
• W2011279072 hasConceptScore W2011279072C149782125 @default.
• W2011279072 hasConceptScore W2011279072C150921843 @default.
• W2011279072 hasConceptScore W2011279072C154945302 @default.
• W2011279072 hasConceptScore W2011279072C169258074 @default.
• W2011279072 hasConceptScore W2011279072C19499675 @default.
• W2011279072 hasConceptScore W2011279072C207609745 @default.
• W2011279072 hasConceptScore W2011279072C33923547 @default.
• W2011279072 hasConceptScore W2011279072C41008148 @default.
• W2011279072 hasIssue "1-2" @default.
• W2011279072 hasLocation W20112790721 @default.
• W2011279072 hasOpenAccess W2011279072 @default.
• W2011279072 hasPrimaryLocation W20112790721 @default.
• W2011279072 hasRelatedWork W2375771286 @default.
• W2011279072 hasRelatedWork W2896146092 @default.
• W2011279072 hasRelatedWork W3116896278 @default.
• W2011279072 hasRelatedWork W4225360065 @default.
• W2011279072 hasRelatedWork W4282839226 @default.
• W2011279072 hasRelatedWork W4283016678 @default.
• W2011279072 hasRelatedWork W4319983935 @default.
• W2011279072 hasRelatedWork W4323021782 @default.
• W2011279072 hasRelatedWork W5646447 @default.
• W2011279072 hasRelatedWork W3143315574 @default.
• W2011279072 hasVolume "23" @default.
• W2011279072 isParatext "false" @default.
• W2011279072 isRetracted "false" @default.
• W2011279072 magId "2011279072" @default.
• W2011279072 workType "article" @default.