strength

strength.wsrf

Give the measure for the collective performance of
 individual trees in the forest model built from <code>wsrf</code>.

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.

He Zhao

wsrf

Weighted Subspace Random Forest for Classification

Qinghan Meng

Junchao Lv

Baoxun Xu

strength.wsrf function

<dl>
 <dt>object</dt>
<dd>object of class <code>wsrf</code>.</dd>
 <dt>...</dt>
<dd>optional additional arguments. At present no additional
 arguments are used.</dd>
</dl>

Arguments

He Zhao and Graham Williams (SIAT, CAS)

Author

Strength — strength.wsrf

<dl>


 <dt>object</dt>
<dd>object of class <code>wsrf</code>.</dd>


 <dt>...</dt>
<dd>optional additional arguments. At present no additional
 arguments are used.</dd>


</dl>

strength.wsrf: Strength

Description

Usage

Value

Arguments

Author

Details

References

See Also