wsrf: An R Package for Classification with Scalable Weighted Subspace Random Forests.

	wsrf: An R Package for Classification with Scalable Weighted Subspace Random Forests.
	Zhao, He; Williams, Graham J.; Huang, Joshua Zhexue
刊名	JOURNAL OF STATISTICAL SOFTWARE
	2017
文献子类	期刊论文
英文摘要	We describe a parallel implementation in R of the weighted subspace random forest algorithm (Xu, Huang, Williams, Wang, and Ye 2012) available as the wsrfpackage. A novel variable weighting method is used for variable subspace selection in place of the traditional approach of random variable sampling. This new approach is particularly useful in building models for high dimensional data - often consisting of thousands of variables. Parallel computation is used to take advantage of multi-core machines and clusters of machines to build random forest models from high dimensional data in considerably shorter times. A series of experiments presented in this paper demonstrates that wsrf is faster than existing packages whilst retaining and often improving on the classificationperformance, particularly for high dimensional data.
URL标识	查看原文
语种	英语
内容类型	期刊论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/12565]
专题	深圳先进技术研究院_数字所
作者单位	JOURNAL OF STATISTICAL SOFTWARE
推荐引用方式 GB/T 7714	Zhao, He,Williams, Graham J.,Huang, Joshua Zhexue. wsrf: An R Package for Classification with Scalable Weighted Subspace Random Forests.[J]. JOURNAL OF STATISTICAL SOFTWARE,2017.
APA	Zhao, He,Williams, Graham J.,&Huang, Joshua Zhexue.(2017).wsrf: An R Package for Classification with Scalable Weighted Subspace Random Forests..JOURNAL OF STATISTICAL SOFTWARE.
MLA	Zhao, He,et al."wsrf: An R Package for Classification with Scalable Weighted Subspace Random Forests.".JOURNAL OF STATISTICAL SOFTWARE (2017).