Random ferns method implementation for the general-purpose machine learning

Details Random ferns method implementation for the general-purpose machine learning Random ferns method implementation for the general-purpose machine learning

2012-02-06

arxiv.org/abs/1202.1121v1

Computer Science

Learning

Scientific paper

In this paper I present an extended implementation of the Random ferns algorithm contained in the R package rFerns. It differs from the original by the ability of consuming categorical and numerical attributes instead of only binary ones. Also, instead of using simple attribute subspace ensemble it employs bagging and thus produce error approximation and variable importance measure modelled after Random forest algorithm. I also present benchmarks' results which show that although Random ferns' accuracy is mostly smaller than achieved by Random forest, its speed and good quality of importance measure it provides make rFerns a reasonable choice for a specific applications.

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