Random Forester (RerF) is an algorithm developed by Tomita (2016)
which is similar to Random Forest  Random
Combination (ForestRC) developed by Breiman (2001)
. Random Forests create axisparallel, or
orthogonal trees. That is, the feature space is recursively split along
directions parallel to the axes of the feature space. Thus, in cases in
which the classes seem inseparable along any single dimension, Random
Forests may be suboptimal. To address this, Breiman also proposed and
characterized ForestRC, which uses linear combinations of coordinates
rather than individual coordinates, to split along. This package,
'rerf', implements RerF which is similar to ForestRC. The difference
between the two algorithms is where the random linear combinations
occur: ForestRC combines features at the per tree level whereas RerF
takes linear combinations of coordinates at every node in the tree.

Maintainer  
License  Apache License 2.0  file LICENSE 
Version  1.1.3.9000 
URL 
https://github.com/neurodata/RRerF

Package repository  View on GitHub 
Installation 
Install the latest version of this package by entering the following in R:
install.packages("devtools")
library(devtools)
install_github("neurodata/RRerF")

neurodata/RRerF documentation built on Sept. 7, 2018, 11:29 p.m.