|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.|
Page views:: 3567. Submitted: 2012-03-01. Published: 2014-11-13.
rFerns: An Implementation of the Random Ferns Method for General-Purpose Machine Learning
This work is licensed under the licenses
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.