wsrf: Weighted Subspace Random Forest for Classification

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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.

Author
Qinghan Meng [aut], He Zhao [aut, cre], Graham Williams [aut], Junchao Lv [ctb], Baoxun Xu [aut]
Date of publication
2016-10-28 10:51:22
Maintainer
He Zhao <Simon.Yansen.Zhao@gmail.com>
License
GPL (>= 2)
Version
1.7.0
URLs

View on CRAN

Man pages

combine.wsrf
Combine Ensembles of Trees
correlation.wsrf
Correlation
importance.wsrf
Extract Variable Importance Measure
oob.error.rate.wsrf
Out-of-Bag Error Rate
predict.wsrf
Predict Method for 'wsrf' Model
print.wsrf
Print Method for 'wsrf' model
strength.wsrf
Strength
subset.wsrf
Subset of a Forest
varCounts.wsrf
Number of Times of Variables Selected as Split Condition
wsrf
Build a Forest of Weighted Subspace Decision Trees

Files in this package

wsrf
wsrf/inst
wsrf/inst/NEWS.Rd
wsrf/inst/doc
wsrf/inst/doc/wsrf-guide.Rmd
wsrf/inst/doc/wsrf-guide.R
wsrf/inst/doc/wsrf-guide.html
wsrf/tests
wsrf/tests/wsrftest.Rout.save
wsrf/tests/wsrftest.R
wsrf/src
wsrf/src/wsrf.h
wsrf/src/Makevars
wsrf/src/rforest.h
wsrf/src/tree.cpp
wsrf/src/rforest.cpp
wsrf/src/node.h
wsrf/src/c4_5_var_selector.cpp
wsrf/src/meta_data.cpp
wsrf/src/utility.h
wsrf/src/tree.h
wsrf/src/IGR.h
wsrf/src/var_selector.h
wsrf/src/dataset.h
wsrf/src/c4_5_var_selector.h
wsrf/src/dataset.cpp
wsrf/src/IGR.cpp
wsrf/src/sampling.h
wsrf/src/sampling.cpp
wsrf/src/Makevars.win
wsrf/src/meta_data.h
wsrf/src/wsrf.cpp
wsrf/NAMESPACE
wsrf/R
wsrf/R/correlation.wsrf.R
wsrf/R/varCounts.wsrf.R
wsrf/R/print.wsrf.R
wsrf/R/combine.wsrf.R
wsrf/R/wsrf.default.R
wsrf/R/strength.wsrf.R
wsrf/R/subset.wsrf.R
wsrf/R/oob.error.rate.wsrf.R
wsrf/R/importance.wsrf.R
wsrf/R/wsrf.formula.R
wsrf/R/predict.wsrf.R
wsrf/R/wsrf.R
wsrf/vignettes
wsrf/vignettes/wsrf-guide.Rmd
wsrf/vignettes/wsrf-guide.bib
wsrf/README.md
wsrf/MD5
wsrf/build
wsrf/build/vignette.rds
wsrf/DESCRIPTION
wsrf/man
wsrf/man/print.wsrf.Rd
wsrf/man/combine.wsrf.Rd
wsrf/man/strength.wsrf.Rd
wsrf/man/predict.wsrf.Rd
wsrf/man/correlation.wsrf.Rd
wsrf/man/subset.wsrf.Rd
wsrf/man/oob.error.rate.wsrf.Rd
wsrf/man/importance.wsrf.Rd
wsrf/man/varCounts.wsrf.Rd
wsrf/man/wsrf.Rd