Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
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Authors: Miron B. Kursa
Title: rFerns: An Implementation of the Random Ferns Method for General-Purpose Machine Learning
Abstract: Random ferns is a very simple yet powerful classification method originally introduced for specific computer vision tasks. In this paper, I show that this algorithm may be considered as a constrained decision tree ensemble and use this interpretation to introduce a series of modifications which enable the use of random ferns in general machine learning problems. Moreover, I extend the method with an internal error approximation and an attribute importance measure based on corresponding features of the random forest algorithm. I also present the R package rFerns containing an efficient implementation of this modified version of random ferns.

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Paper: rFerns: An Implementation of the Random Ferns Method for General-Purpose Machine Learning     Download PDF (Downloads: 5247)
Supplements:
rFerns_1.0.0.tar.gz: R source package Download (Downloads: 207; 13KB)
v61i10.R: R example code from the paper Download (Downloads: 275; 752B)
v61i10-assessment.zip: Replication materials for assessment section Download (Downloads: 196; 477KB)
DOI: 10.18637/jss.v061.i10

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.