rFerns: An implementation of the random ferns method for general-purpose machine learning

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. © 2014,american statistical association. all right reserved.