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.
| Version: |
1.5.29 |
| Depends: |
R (≥ 3.0.0), Rcpp (≥ 0.10.2), stats, parallel |
| LinkingTo: |
Rcpp |
| Suggests: |
rattle (≥ 2.6.26), randomForest (≥ 4.6.7), party (≥
1.0.7), stringr (≥ 0.6.2), knitr (≥ 1.5) |
| Published: |
2015-10-10 |
| Author: |
Qinghan Meng [aut],
He Zhao [aut, cre],
Graham Williams [aut],
Junchao Lv [ctb],
Baoxun Xu [aut] |
| Maintainer: |
He Zhao <Simon.Yansen.Zhao at gmail.com> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
yes |
| Materials: |
README NEWS |
| CRAN checks: |
wsrf results |