Evaluation of most similar neighbour and random forest methods for imputing forest inventory variables using data from target and auxiliary stands
By Hooman Latifi, Barbara Koch. 2012
50 citations recorded by the source corpus.
Subfields: Environmental Engineering, Insect Science, Nature and Landscape Conservation
View Paper: https://www.semanticscholar.org/paper/d7d426295018fe8a4601ae6a8d671a04183ad17e
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In Environmental Engineering
In Insect Science
In Nature and Landscape Conservation
Citations in the corpus
- Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass (2014, unknown): “Some studies used other machine learning methods such as support vector machines (SVM) (e.g., Chen, Hay, & Zhou, 2010; Zhao, Popescu, Meng, Pang, & Agca, 2011) and random forest (RF) (e.g., Latifi & Koch, 2012; Yu, Hyyppa, Vastaranta, Holopainen, & Viitala, 2011).”
- Amphibians over the edge: silent extinction risk of Data Deficient species (2014, methodology): “RandomForest imputation has been shown to be robust in assigning meaningful and accurate values to missing data points (Breiman, 2003; Cutler et al., 2007; Pantanowitz & Marwala, 2009; Latifi & Koch, 2012).”