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Found 6 papers, 1 papers with code
Generalised Random Forest Space Overview
Assuming a view of the Random Forest as a special case of a nested ensemble of interchangeable modules, we construct a generalisation space allowing one to easily develop novel methods based on this algorithm.
Multi-label Ferns for Efficient Recognition of Musical Instruments in Recordings
In this paper we introduce multi-label ferns, and apply this technique for automatic classification of musical instruments in audio recordings.
A Comparison of Random Forests and Ferns on Recognition of Instruments in Jazz Recordings
In this paper, we first apply random ferns for classification of real music recordings of a jazz band.
Robustness of Random Forest-based gene selection methods
Gene selection is an important part of microarray data analysis because it provides information that can lead to a better mechanistic understanding of an investigated phenomenon.
rFerns: An Implementation of the Random Ferns Method for General-Purpose Machine Learning
In this paper I present an extended implementation of the Random ferns algorithm contained in the R package rFerns.
Feature Selection with the Boruta Package
This article describes a R package Boruta, implementing a novel feature selection algorithm for finding all relevant variables.
