rFerns: An Implementation of the Random Ferns Method for General-Purpose Machine Learning

Miron B. Kursa

Journal of Statistical Software, 2014, vol. 061, issue i10

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.

Date: 2014-11-13
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Persistent link: https://EconPapers.repec.org/RePEc:jss:jstsof:v:061:i10

DOI: 10.18637/jss.v061.i10

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