wsrf: Weighted Subspace Random Forest for Classification

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.

AuthorQinghan Meng [aut], He Zhao [aut, cre], Graham Williams [aut], Junchao Lv [ctb], Baoxun Xu [aut]
Date of publication2016-10-28 10:51:22
MaintainerHe Zhao <Simon.Yansen.Zhao@gmail.com>
LicenseGPL (>= 2)
Version1.7.0
https://github.com/SimonYansenZhao/wsrf,http://togaware.com

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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

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