R package wsrf. 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) <<a href=”http://dx.doi.org/10.4018/jdwm.2012040103”>doi:10.4018/jdwm.2012040103</a>>. 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.
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References in zbMATH (referenced in 1 article , 1 standard article )
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- He Zhao and Graham Williams and Joshua Huang: wsrf: An R Package for Classification with Scalable Weighted Subspace Random Forests (2017)