In summary, the RF works by constructing multiple decision (or classification) trees, with each tree providing a separate classification for an input feature vector.
Random forest creates multiple classification and regression (CART) trees, each trained on a bootstrap sample of the original training data and searches across a randomly selected subset of input variables to determine the split.
RF will create multiple classification and regression (CART) trees, each trained on a bootstrap sample of the original training data and searches across a randomly selected subset of input variables to determine the split.
Two automated statistical procedures were also applied: a backward variable selection approach applied to a parametric penalised logistic regression model with multiple interaction terms and non-parametric classification trees.
Models designed through multiple memberships multiple classification, 34 or using a mixture of Polya trees, 35 or multivariate CAR 23 offer desirable properties.
This sample classification method is designed, through multivariate analysis, to construct classification trees recognizing a complex pattern of multiple peak intensities.
As first developed, CART, could lead to quite large tree models, but recent work has incorporated p-value based tree modelling, known as conditional classification trees, which yield smaller tree models whilst simultaneously controlling for multiple testing and are available in the Party Package [ 23] in R [ 24].
We used statistical softwares S-Plus 4.5 (Statsci. Seattle, WA) for constructing regression and classification trees and STATA 6.0 (Stata Corporation, College Station, TX) for performing multiple logistic regression.
However, the knowledge-based model was much more time-intensive than the classification trees, and failed to classify all pixels within the study area even after multiple iterations of the model.
