Weighted Subspace Random Forest for Classification
Combine Ensembles of Trees
Correlation
Extract Variable Importance Measure
Out-of-Bag Error Rate
Predict Method for wsrf Model
Print Method for wsrf Model
Strength
Subset of a Forest
Number of Times of Variables Selected as Split Condition
Build a Forest of Weighted Subspace Decision Trees
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.
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