Weighted Subspace Random Forest for Classification
Description
A parallel implementation of Weighted Subspace Random
Forest. The Weighted Subspace Random Forest algorithm was
proposed in the International Journal of Data Warehousing and
Mining, 8(2):44-63, 2012, proposed by Baoxun Xu, Joshua Zhexue
Huang, Graham Williams, Qiang Wang, and Yunming Ye. The algorithm
can classify very high-dimensional data with random forests built
using small subspaces. A novel variable weighting method is used
for variable subspace selection in place of the traditional random
variable sampling.This new approach is particularly useful in
building models from high-dimensional data.