Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 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. 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.

7
The categories of this paper are: hep-th
12
The categories of this paper are: nucl-th
17
The categories of this paper are: q-fin
29
The categories of this paper are: funct-an
28
The categories of this paper are: alg-geom
23
The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
11
The categories of this paper are: hep-lat
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph