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 by Baoxun Xu, Joshua Zhexue Huang, Graham Williams, Qiang
Wang, and Yunming Ye (2012) <doi:10.4018/jdwm.2012040103>. 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: |
1.5.47 |
| 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: |
2016-07-11 |
| 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> |
| BugReports: |
https://github.com/SimonYansenZhao/wsrf/issues |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: |
https://github.com/SimonYansenZhao/wsrf |
| NeedsCompilation: |
yes |
| Materials: |
README NEWS |
| CRAN checks: |
wsrf results |