rFerns: An Implementation of the Random Ferns Method for General-Purpose Machine Learning
This work provides an incremental improvement to the Random Ferns method, making it more practical for general-purpose machine learning tasks with mixed data types.
The authors extended the Random Ferns algorithm to handle categorical and numerical attributes (not just binary) and incorporated bagging with error approximation and variable importance measures. Benchmark results showed Random Ferns has lower accuracy than Random Forest but offers faster speed and useful importance measures for specific applications.
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.