Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction

By Anantha M. Prasad, Louis Iverson, Andy Liaw. Ecosystems, 2006

2,256 citations recorded by the source corpus.

Subfields: Atmospheric Science, Ecological Modeling, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/44d7982a1e6cf9646547a902124ab6ed1a80aac5

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Citations in the corpus

Predicting climate‐induced range shifts: model differences and model reliability (2006, methodology): “Prasad et al. (2006) found that random forest models and bagging (another tree-based model-averaging approach) consistently produced better predictions than multivariate adaptive regression splines and regression trees for predicting the distribu-Absent Stable Expansion Contraction GLM TREE GAM”
Predicting species distributions: a critical comparison of the most common statistical models using artificial species (2007, methodology): “This may be a more realistic assumption for many species and their limiting resources, since it is likely that many environmental factors interact in determining speciesoccurrence patterns (Prasad et al., 2006).”
Potential Impacts of Climate Change on the Distribution of North American Trees (2007, background): “This problem is exacerbated in rarer species because of low source strength (Iverson et al. 2004b), making them more prone to extinction (Schwartz et al. 2006).”
Application of machine learning methods to spatial interpolation of environmental variables (2011, methodology): “Although the unpruned individual trees will result in overfitted models (Prasad et al., 2006), our results of RF based on unpruned trees suggest that the overfitting of individual trees may not be a big concern to RF. RF does not overfit with respect to the source data because of the law of large…”
Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery (2017, background): “Prasad et al. [31] stated that DT is too sensitive to small changes in the training dataset.”
Does Garbage in the Diet Improve Reproductive Output of Glaucous Gulls? (2010, methodology): “Random forest is a regression-tree algorithm that develops a model to predict to a continuous target variable (Prasad et al. 2006, Cutler et al. 2007).”
Habitat suitability modelling of an invasive plant with advanced remote sensing data (2009, methodology): “…aggregation algorithms, including bagged decision trees, boosted decision trees, and random forests, have been shown to perform consistently among the set of optimal techniques for species distribution modelling (Elith et al., 2006; Prasad et al., 2006; Cutler et al., 2007; Guisan et al., 2007).”
A working guide to boosted regression trees. (2008, neutral): “2001; Reineking & Schröder 2006).”
A tutorial review: Metabolomics and partial least squares-discriminant analysis--a marriage of convenience or a shotgun wedding. (2015, background): “The following advantages of random forests have been reported in the literature [33,126–136], including its ability to: deal with large datasets without variable deletion; provide a feature importance measure of the predictor variables (mean decrease in accuracy); a measure of the internal structure”
Random forest predictive modeling of mineral prospectivity with small number of prospects and data with missing values in Abra (Philippines) (2015, background): “* Email for correspondence: john.carranza@jcu.edu.au”